Systems and methods for quantitative phenotyping of fibrosis

ABSTRACT

Systems and methods are provided for computer aided phenotyping of fibrosis-related conditions. A digital image indicates presence of collagens in a biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the collagens that is expected to be different for different phenotypes of fibrosis. At least some features are tissue level features that describe macroscopic characteristics of the collagens, morphometric level features that describe morphometric characteristics of the collagens, and texture level features that describe an organization of the collagens. At least some of the plurality of parameters are statistics associated with histograms corresponding to distributions of the associated parameters across at least some of the digital image. At least some of the plurality of parameters are combined to obtain one or more composite scores that quantify a phenotype of fibrosis for the biological tissue sample.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. provisionalapplication Ser. No. 62/852,745, filed May 24, 2019, which is herebyincorporated by reference in its entirety. This application is relatedto PCT Application Ser. No. ______ [Attorney Docket No.1151183-0002-001-WO1], filed Apr. 17, 2020, which is hereby incorporatedby reference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to quantitative assessment of fibrosisincluding, without limitation, computerized systems and methods thatanalyze a digital image taken of a biological tissue sample toquantitatively evaluate selected parameters that correlate with thephenotype of fibrosis, to quantify traits of the fibrosis phenotype inthe biological tissue sample.

BACKGROUND

Fibrosis refers to the accumulation of collagen-based fibrous tissue inan organ when the organ attempts to repair and replace damaged cells butcreates non-functioning scar tissue in place of functional tissue. Thereare different stages of fibrosis, and in its most severe form, the scartissue destroys the organ's internal structure and, in the case of theliver, impairs its ability to regenerate. In the liver, this is referredto as cirrhosis and can cause portal hypertension, which can result inpainful swelling and bleeding.

There are a variety of fibrotic conditions in multiple organs, such asliver, lung, kidney, heart, skin, uterus, muscle, adipose tissues,tissue from the gastric intestinal tract, and cancerous tissues in bothhuman and animals. Some in-vitro models and meso-biological systems suchas spheroids or printed organs can also develop fibrotic conditions. Tosome extent, the collagen-rich fibrous tissue (fibrosis) is necessary tostructure organs and tissues because of its scaffold structure.

For liver, Non-Alcoholic SteatoHepatitis (NASH) is a severe form ofnon-alcoholic fatty liver disease (NAFLD) that can result in fibrosisand cirrhosis. While NASH is closely related to obesity, pre-diabetes,and diabetes, its symptoms are often non-specific to NASH, which makesit difficult to diagnose. Often, NASH patients are unaware of theircondition until late stages of the disease. There are currently nomedical treatments for NASH yet, but drug discovery efforts against NASHare ongoing.

For the lung, Idiopathic Pulmonary Fibrosis (IPF) is a type of chronicscarring of the lungs characterized by an irreversible progression offibrosis resulting in a decline in lung function and death bysuffocation. There are currently no satisfactory medical treatments forIPF yet, but drug discovery efforts against IPF are ongoing.

For skin, Scleroderma is a group of diseases that cause abnormal growthof the connective tissues due to excessive collagen (fibrosis)generation. Symptoms of scleroderma include calcium deposits inconnective tissues, narrowing of blood vessels in the hands or feet,swelling of the esophagus, thick, tight skin on the fingers, and redspots on hands and face, all creating significant patient discomfort andhandicaps. The causes of scleroderma are unknown, and there is no curefor scleroderma. However, various treatments can control and/or slowsymptoms and complications. Drug discovery efforts against Sclerodermaare ongoing.

To assess the extent of organ damage due to fibrosis, physicians canperform non-invasive testing such as blood tests and imaging tests, butthe gold standard is to perform a biopsy by removing a tissue samplefrom the organ and using histopathology methods to evaluate the sample.This includes (1) fixing the tissue from the biopsy; (2) embedding thetissue in paraffin blocks; (3) sectioning the paraffin blocks to obtainthin sections of the tissue (typically 5 microns); (4) staining thesections with pathology stains; (5) imaging the stained tissues/sectionsby white light microscopy or digital white light microscopy; (6)quantifying specific tissue features, either by a pathologist, anautomated image analysis, or both.

Pathologists use categorical scoring systems to assess the tissue biopsyand determine the extent of fibrosis. The METAVIR scoring system iscommonly used to assess the liver biopsy to determine the extent offibrosis in patients with hepatitis C. The NAKANUMA system is used toquantify the severity of fibrosis in Primary biliary cholangitis,another liver disease with no treatment. The ASHCROFT scale is used fordetermining the degree of fibrosis in lung specimens.

The NASH-CRN Fibrosis system classifies liver fibrosis in NASH patientsinto five stages, ranging from F0 to F4 (as indicated in the tablebelow) and representing different amounts of fibrosis or scarring.

F0 No fibrosis, no scarring F1 Minimal scarring, or portal fibrosiswithout septa F2 Significant fibrosis, scarring has occurred and extendsoutside the liver area, portal fibrosis with few septa F3 Severfibrosis, fibrosis spreading and forming bridges with other fibroticliver areas, numerous septa without cirrhosis F4 Advanced scarring,cirrhosis

However, the NASH-CRN Fibrosis scoring system is coarse in that it onlydefines five categories that range from no fibrosis (F0) to cirrhosis(F4). The scoring system is unable to distinguish between patients thatare in one stage (e.g., F3), but are on opposite ends of that stage(e.g., closer to F2 or F4). For patients that are on opposite ends ofthe same stage, the plan for treatment may be different.

Similarly, all the current histological categorical scales used for thequantification of fibrosis are coarse and do not distinguish betweenpatients on opposite ends of the same category. The same applies tohistological systems used for the assessment of fibrosis in all kinds ofbiological tissues.

Moreover, current histological systems for the assessment of fibrosis infibrosis-related conditions lack of accuracy and reproducibility. Theyare prone of a very high (up to 35%) intra- and inter-operatorvariability because these systems mostly relay on the elevation andscoring of histological by pathologists, which have variable level ofexperience, training or belong to different schools of thoughts.

Current methods apply the same general fibrosis stages that are used inhumans (e.g., F0 through F4) for pre-clinical studies on animals. Thisassumes the scales developed on human studies are applicable to modelthe different stages and progression of fibrosis on animal models. Thisis problematic because, for example, some animal models for fibrosis donot exhibit septa, is used in part to define the F3 stage.

Current histological systems for the quantification of fibrosis arebased on a very limited set of observable parameters, and are verypoorly quantified by the human eye or simplistic quantification systemsand exhibit very poor detection thresholds. They cannot quantify complexfibrosis phenotypes and, as a result, their utility is significantlylimited to the staging of disease severity. This poses problems in thediscovery and development of new drugs, or in the assessment andclassification of patients to guide the management of their conditions.

Accordingly, current methods of measuring fibrosis are too simplisticand coarse (e.g., only four to six stages) to be able to distinguisheven moderate changes in fibrosis, are subject to high intra- andinter-operator variability, and make assumptions regarding thesimilarity between human and animal models. Their performance limits thedevelopment of new drugs and biomarkers for fibrosis diseases, and thelack of phenotypic relevance limits the management of multiple forms ofthe fibrotic conditions across multiple species.

SUMMARY OF THE INVENTION

It is a goal of the present disclosure to improve upon the coarsemethods of measuring fibrosis by implementing a phenotypic andcontinuous scoring system that provides increased dynamic range, andimproved detection thresholds and incremental resolution to quantify thefibrosis phenotypes and their differences. The present disclosureprovides systems and methods for tracking fibrotic disease progressionon small time-scales, can be used to track the regression of fibrosis inresponse to a therapeutic compound or change in diet, and are suitablefor describing the effect of investigational compounds in pre-clinicalor clinical studies. In contrast to coarse stages of fibrosis, thepresent disclosure's phenotypic continuous scoring system distinguishesbetween the fibrosis phenotypes between two genetically different poolsof animals or patients, and can distinguish between fibrosis conditionsthat have different aetiologies (for instance, liver fibrosis can beseen as a complication of obesity, diabetes, alcoholic steatosis,non-alcoholic steatosis, Hepatitis (B or C), etc.).

Because fibrosis is expressed as multiple forms and features acrossmultiple organs and biological tissues, it is appropriate to describeand quantify fibrosis in a phenotypic way, using its compositeobservable characteristics or traits.

Accordingly, the present disclosure provides a way to measure fibrosisthat has a wide dynamic range and fine resolution, because it has acontinuous scale and a very high signal to noise ratio. Because thepresent disclosure incorporates a large number of selected quantifiableparameters to establish a composite score, the composite score providesa very high signal-to-noise ratio, resulting in high detectionthresholds, improved sensitivity, and large dynamic ranges. As a result,the present disclosure is less prone to errors due to sources of noisefrom tissue processing or image acquisition. The quantitative nature ofthe present disclosure also ensures the system for measuring fibrosis isautomated and not prone to large intra- and inter-operator variability.Moreover, the present disclosure is optimized for specific populations,such as pediatric versus adult patient populations, or specific animalmodels, and can even further be optimized for a specific organ in humansor a specific type of animal or biological tissue.

Accordingly, the phenotypic fibrosis scoring systems and methodspresented herein can be used to do any of the following: quantify theseverity and progression of fibrosis in a patient (or in anotherbiological system), assess the response (or lack of thereof) of apatient (or any other biological system) to a therapeutic compound onshort time-scales, and quantify the phenotype of fibrosis and itsdifferences across species and genetic phenotypes, diseases conditions,stages, groups and sub-groups and aetiologies, treatments and response.

It is further an object of the present disclosure to quantify thephenotype of fibrosis in a biological tissue sample that considersmultiple, if not all, the degrees of complexity of the collagens and itstraits. As used herein, “fibrosis” includes all kinds of collagen-basedstructures, regardless of the chemical nature of the collagen. As usedherein, “collagen” includes different types of collagens creatingfibrosis, including collagen I, collagen III, etc. As used herein,“collagen object” corresponds to a representation of collagen, asdepicted in a digital image. Identification of the collagen object in adigital image may result from pre-processing the digital image toindicate adjacent pixels or regions of the digital image that correspondto collagen. The collagen object may reflect adjacent pixels or regionsof the digital image that correspond to the same type of collagen, andmay be extracted from the digital image. As used herein, “collagens”corresponds to one or more collagen objects represented in a digitalimage, and may include the same or different types of collagen.

Described herein are systems and methods for quantifying a fibrosisphenotype. One aspect relates to computer aided phenotyping offibrosis-related conditions. A digital image of a biological tissuesample is received, wherein the digital image indicates presence ofcollagens in the biological tissue sample. The image is processed toquantify a plurality of parameters, each parameter describing a featureof the collagens in the biological tissue sample that is expected to bedifferent for different phenotypes of fibrosis. At least some of thefeatures are selected from at least two of the group consisting of: (1)tissue level features that describe macroscopic characteristics of thecollagens depicted in the digital image of the biological tissue sample;(2) morphometric level features that describe morphometriccharacteristics of the collagens depicted in the digital image of thebiological tissue sample; and (3) texture level features that describean organization of the collagens depicted in the digital image of thebiological tissue sample. At least some of the plurality of parametersare statistics associated with histograms corresponding to distributionsof the associated parameters across at least some of the digital image.At least some of the plurality of parameters in (b) are combined toobtain one or more composite scores that quantify a phenotype offibrosis for the biological tissue sample.

In some implementations, at least one of the features is a tissue levelfeature, at least another feature in the features is a morphometriclevel feature, and at least another feature in the features is a texturelevel feature. At least two of the features may be tissue levelfeatures, at least another two features in the features may bemorphometric level features, and at least another two features in thefeatures may be texture level feature.

In some implementations, the at least some of the plurality ofparameters that are combined corresponds to all of the plurality ofparameters.

In some implementations, a first composite score of the one or morecomposite scores is specific to a feature type selected from the groupconsisting of tissue level, morphometric level, and texture level. Asecond composite score of the one or more composite scores may bespecific to another feature type selected from the group consisting oftissue level, morphometric level, and texture level. The first andsecond composite scores may be combined to quantify the phenotype offibrosis for the biological tissue sample.

In some implementations, the systems and methods are compatible with anymodality of imaging that distinguishes between a presence and absence ofcollagens in the biological tissue sample. The systems and methods maybe compatible with at least stained histopathology slides, two photonmicroscopy, fluorescence imaging, structured imaging, polarized imaging,CARS, OCT images, fresh tissue imaging, and endoscopy.

In some implementations, the depiction of collagens in the biologicaltissue sample results from an optical marker that is specific to anyform of collagen. The optical marker may be selected from at least oneof the group consisting of: (1) a collagen-specific stain used in ahistopathology method; and (2) an intrinsic bio-optical marker specificto one or more collagens that is intrinsic to a modality of the digitalimage.

In some implementations, pixels of the digital image indicate presenceand quantity of collagens in corresponding volumes of the biologicaltissue sample.

In some implementations, each of the texture level featurescharacterizes different regions of the digital image by describing thecollagen of each region. The different regions may correspond tonon-overlapping or overlapping sample windows of the digital image.Quantifying a parameter describing a texture level feature may comprise:obtaining a sample value for each sample window, to obtain a pluralityof sample values; generating a histogram of the plurality of samplevalues; and computing a statistic of the histogram as the parameterdescribing the texture level feature. The sample windows have the sameor different sizes for different texture level features.

In some implementations, at least some of the parameters describing amorphometric level feature or a texture level feature is one of thestatistics associated with histograms. In some cases, no parametersdescribing tissue level features are statistics associated withhistograms. Sample values of the histograms for morphometric levelfeatures may correspond to individual fibers or bundles of fibers.Sample values of the histograms for texture level features maycorrespond to sample windows that identify subsets of the image. Atleast some of the plurality of parameters may result from processing thehistograms.

In some implementations, the statistics associated with histograms areselected from the group consisting of (1) trend statistics includingmean, median, mode, and normalized count, (2) distortion statisticsincluding skew, (3) variance statistics including standard deviation,variance, outliers, and kurtosis, and (4) transition statistics relatedto cut-off values. The cut-off values may split the associated histograminto subsets of sample values, and each transition statistic is for asingle subset of sample values. At least one of the histograms may besplit by multiple cut-off values. The cut-off values may be determinedby a user. The cut-off values may be different for different tissues. Atleast some statistics associated with histograms may be selected fromthe group consisting of trend statistics, distortion statistics, andvariance statistics of individual subsets of sample values. Statisticsassociated with histograms may include normalized counts of individualsubsets of sample values, wherein the normalized counts represent thenumber of sample values within the respective individual subset,normalized by the unit surface area of the digital image.

In some implementations, quantifying at least some of the plurality ofparameters associated with histograms comprises processing thehistograms to identify multiple modes of the histograms. Processing ahistogram to identify multiple modes may comprise deconvoluting thehistogram. Some modes of the histograms may correspond to phenotypicsignatures of the fibrosis-related conditions, wherein deconvoluting thehistogram comprises filtering the histogram to determine whether thehistogram exhibits a phenotypic signature and to quantify the exhibitedphenotypic signature.

In some implementations, the parameters that describe tissue levelfeatures include at least one selected from the group consisting of:collagen area ratio, large collagen object normalized density, smallcollagen object normalized density, and collagen network reticulationindex.

In some implementations, the parameters that describe morphometric levelfeatures include at least one selected from the group consisting of:length, skeleton length, eccentricity, solidity, curvature ration, area,perimeter, and collagen density.

In some implementations, the parameters that describe texture levelfeatures include at least one relating to a collagen image pixelintensity level co-occurrence matrix, including at least one of thegroup consisting of: energy, homogeneity, correlation, inertia, entropy,skewness, and kurtosis.

In some implementations, the digital image is processed to distinguishbetween collagens in the biological tissue sample represented in thedigital image. In this case, each of the plurality of parameters used toobtain at least one of the one or more composite scores may correspondto one class of collagen. The collagen classes may include one or moreof fine collagen, assembled collagen, and tissue regions. Each of theplurality of parameters used to obtain the at least one of the one ormore composite scores may correspond to the collagen class of finecollagen. Each of the plurality of parameters used to obtain the atleast one of the one or more composite scores may correspond to thecollagen class of assembled collagen. The plurality of parameters usedto obtain the at least one of the one or more composite scores maycorrespond to both collagen classes of fine collagen and assembledcollagen. The collagen classes may include one or more of long collagensand short collagens. The collagen classes may include one or more ofhigh textured regions and low textured regions. The collagen classes mayrepresent different levels of complexity in collagen skeleton.

In some implementations, at least some of the parameters describingmorphometric features relate to one collagen class. In someimplementations, at least some of the parameters describing tissue levelfeatures relate to one collagen class. In some implementations, thecollagen classes distinguish different stages of fibrosis.

In some implementations, different features are selected to quantifydifferent phenotypes of fibrosis. For example, parameters describingfeatures of fine collagen fibers may be used to distinguish Fibrosis 0,Fibrosis 1, and Fibrosis 2. Parameters describing features of assembledcollagen fibers may be used to distinguish Fibrosis 2, Fibrosis 3, andFibrosis 4.

In some implementations, the plurality of parameters that are combinedare selected from a list of candidate parameters. The plurality ofparameters that are combined may be selected from the list of candidateparameters by identifying parameters that distinguish between differentphenotypes of fibrosis. The identified parameters that distinguishbetween different phenotypes of fibrosis may be selected based on acalibration technique. The calibration technique involves a calibrationdata set of calibration digital images taken from biological sampleshaving known phenotypes of fibrosis. In an example, the calibrationtechnique involves principal component analysis to identify the selectedparameters.

In some implementations, the selected parameters have less varianceacross the calibration data set for a given phenotype of fibrosis thanunselected parameters. The known phenotypes of fibrosis may correspondto different outcomes of fibrosis disease. The known phenotypes offibrosis may correspond to disease severity. The known phenotypes offibrosis may correspond to NASH-CRN F disease stage, which include F0,F1, F2, F3, and F4. The known phenotypes of fibrosis correspond todifferent values or ranges of a fibrosis-related biomarker. Thefibrosis-related biomarker may be indicative of progression of fibrosisand/or may be indicative of regression of fibrosis in response totreatment. The known phenotypes of fibrosis may correspond to differentclasses of fibrosis. The different classes of fibrosis may include NASH1 and NASH 2.

In some implementations, the calibration technique selects parametersthat have higher signal-to-noise than parameters that are not selected.Compared to the unselected parameters, the selected parameters mayexhibit higher absolute differences for different phenotypes of fibrosisfor the calibration data set. Compared to the unselected parameters, theselected parameters may exhibit lower standard deviations for the samephenotype of fibrosis for the calibration data set.

In some implementations, the combining of parameters may compriseassigning a weight to each parameter, wherein the absolute weight ishigher for selected parameters that have lower standard deviation forthe same phenotype of fibrosis for the calibration data set than otherselected parameters. The weight may be negative for parameters that arenegatively correlated with progression of fibrosis.

In some implementations, the list of candidate parameters includes over300 candidate parameters, and the selected plurality of parametersconsists of fewer than 35 parameters, fewer than 25 parameters, fewerthan 15 parameters, 5 to 7 parameters, 2 to 5 parameters.

In some implementations, the calibration digital images are taken frombiological samples of humans, wherein the calibration technique isspecific to humans. The biological samples of humans may be taken fromthe same type of organ, wherein the calibration technique is specific tothe same type of organ.

In some implementations, the calibration digital images are taken frombiological samples of the same type of animal, wherein the calibrationtechnique is specific to the same type of animal. The biological samplesof the same type of animal may be taken from the same type of organ,wherein the calibration technique is specific to the same type of organ.The organ may be selected from the group consisting of the liver, lung,kidney, heart, skin, uterus, eye, gut, muscle, adipose tissue, tissuefrom the gastric intestinal tract, cancerous tissues both in human andanimals, or any other organ or portion of the body that is susceptibleto fibrosis.

In some implementations, the fibrosis-related conditions are selectedfrom the group consisting of Hepatitis A, B or C, Non AlcoholicSteatosis Hepatitis (NASH), Alcoholic Steatosis Hepatitis (ASH),Cirrhosis, Primary Biliary Cholangitis and Primary biliary Cirrhosis,Idiopathic Pulmonary Fibrosis, Kidney Chronic Disease, Renal Disease,Scleroderma and Scaring, Duchenne muscular dystrophy, Myocardialinfarction and repair, Macular Degeneracies, Glaucoma Uterine, and anymanifestation of fibrosis in cancer.

In some implementations, the biological tissue sample is taken from asubject. The biological tissue sample may be tissue sampled from anorgan of the subject that is susceptible to fibrosis. The organ may be aliver, lung, kidney, heart, skin, uterus, muscle, adipose tissues,tissue from the gastric intestinal track, cancerous tissues both inhuman and animals, or any other organ or portion of the body that, issusceptible to fibrosis. The subject may be a human, an animal, or ameso-biological system. The meso-biological system includes one or morespheroids, one or more printed organs, or a phenotypically relevantbiosystem that is susceptible to fibrosis.

In some implementations, the digital image is taken of a biologicalcellular sample in vitro.

In some implementations, the severity of fibrosis of the biologicaltissue sample is quantified on a continuous scale. In someimplementations, the progression of fibrosis for the subject isquantified on a continuous scale. In some implementations, theregression of fibrosis for the subject in response to a therapy, isquantified on a continuous scale. In some implementations, the type offibrosis for the subject is quantified on a continuous scale.

In some implementations, effectiveness of a therapy or a drug candidateis evaluated. The therapy or drug candidate may be determined to beeffective in a subject from which the biological tissue sample wastaken. The therapy or drug candidate may be determined to be effectivewhen it is determined that the therapy or drug candidate preventsprogression of fibrosis in the subject.

In some implementations, the effectiveness of a drug candidate or acombination of drug candidates in a plurality of drug candidates isevaluated. The derivation of the one or more composite scores may berepeated for each sample in a plurality of samples, wherein at leastsome of the samples are treated with one of the drug candidates in theplurality of drug candidates. A rank may be assigned to each drugcandidate in the plurality of drug candidates based on the one or morecomposite scores for the plurality of samples. The drug candidate withthe highest rank may be selected as an effective drug candidate.

According to one aspect of the present disclosure, a subject with afibrosis-related condition is treated, by determining one or morecomposite scores indicative of a phenotype of fibrosis for a biologicaltissue sample taken from the subject, using any of the systems andmethods described herein; classifying the patient according to thephenotype in to select a treatment plan involving a therapeutic drug;determining an effective type and amount of the therapeutic drug basedon the one or more composite scores; and administering the determinedamount of the therapeutic drug to the subject.

In some implementations, the aforementioned steps are repeated a numberof times to track progression of the fibrosis-related condition duringtreatment. The amount of the therapeutic drug may be adjusted based onwhether the patient responds to treatment, as determined by the one ormore composite scores.

According to one aspect of the present disclosure, a therapeutic drug isidentified for use in treatment of a fibrosis-related condition, bydetermining one or more composite scores indicative of a predictedphenotype of fibrosis for a biological tissue sample taken from thesubject, using any of the systems and methods described herein;administering a candidate drug to the subject; repeating the determiningstep to determine a fibrosis progression score of the subject over aperiod of time; and determining if the candidate drug is an effectivetherapeutic drug from the fibrosis progression score.

In some implementations, the plurality of parameters are selected fromone or more, 10 or more, 50 or more, 100 or more, 150 or more, 200 ormore, 250 or more, or 300 or more of the quantifiable fibrosisparameters listed in Appendix A.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features of the disclosure, its nature and various advantages,will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 depicts an exemplary system for quantifying a phenotype offibrosis, according to an illustrative implementation;

FIG. 2 is a high-level flow diagram of a process for quantifying aphenotype of fibrosis in a digital image of a biological tissue sample,according to an illustrative implementation;

FIG. 3 depicts a set of exemplary digital images with correspondingexemplary tissue level parameters, according to an illustrativeimplementation;

FIG. 4 depicts an exemplary data structure that stores mean values ofexample tissue level parameters, representing different features ofcollagen depicted in digital images of the calibration data set, takenfrom biological samples having known fibrosis stages in biopsies ofadult patients with Liver NASH (F0, F2, F4), according to anillustrative implementation;

FIGS. 5-6 depict the same set of exemplary digital images as shown inFIG. 3 , with corresponding exemplary morphometric level parameters forthe length (FIG. 5 ) and the eccentricity (FIG. 6 ) of the collagenobjects identified in the digital image, their histogram distributionand some of the related histogram analysis quantitative parameters,according to an illustrative implementation;

FIG. 7 depicts an exemplary data structure that stores the results ofthe histogram analysis of some examples of morphometric levelparameters, representing different features of collagen depicted indigital images of the calibration data set, taken from liver biopsies ofadult patients with NASH having known fibrosis stages (F0, F2, F4),according to an illustrative implementation;

FIG. 8 depicts the same set of exemplary digital images as shown inFIGS. 3 and 5-6 , with corresponding exemplary texture level parameters,their histogram distribution and some of the related histogram analysisquantitative parameters, according to an illustrative implementation;

FIG. 9 depicts an exemplary data structure that stores mean values ofexample texture level parameters, representing different textureorganizational features of collagen depicted in digital images of thecalibration data set, taken from liver biopsies of adult patients withNASH having known fibrosis stages (F0, F2, F4), according to anillustrative implementation;

FIG. 10 is an exemplary flow diagram of a calibration process forselecting parameters to include in a calculation of a composite scorefor quantifying a phenotype for fibrosis for a specific population,according to an illustrative implementation;

FIG. 11 depicts exemplary normalized values for quantitative parametersin a calibration data set for different fibrosis phenotypes for aspecific population, according to an illustrative implementation;

FIG. 12 depicts a plot showing the noise versus rate of change ofvarious quantitative parameters in a calibration data set for a specificpopulation, according to an illustrative implementation;

FIG. 13 depicts a processed version of an exemplary digital image, inwhich certain areas of the image are recognized as collagen (blackpixels) and a collagen object is extracted from the image, according toan illustrative implementation;

FIG. 14 depicts a processed version of an exemplary digital image, inwhich different collagen features, including steatosis, fine (or“perisinusoidal” in the case of liver) collagen, and assembled collagenare extracted, according to an illustrative implementation;

FIG. 15 depicts on the left, a heat map of a set of selectedquantitative parameters (y-axis) describing collagen features in acalibration data set for different populations, including control,untreated, vehicle, and treated at different doses (10, 30, and 100mg/kg), and on the right, a bar graph showing the fibrosis compositescore is able to track the different stages of disease, includingresponse to treatment, according to an illustrative implementation;

FIG. 16 depicts on top, a heat map of a set of selected quantitativeparameters (y-axis) describing collagen features in a calibration dataset for liver biopsies of adult patients with NASH, including F0, F1,F2, F3, and F4 (x-axis), and on the bottom, a bar graph showing thefibrosis composite score is able to track the different stages of thedisease, according to an illustrative implementation;

FIG. 17 depicts data indicating how the composite score, developed toquantify the severity of fibrosis in adult patients suffering fromPrimary Biliary Cholangitis, correlates with the Nakanuma fibrosisstages (FIG. 17A), and provides a continuous assessment of changes (inthis case reduction) of fibrosis in a cohort of patients from baselineto follow up (after treatment) biopsies (FIG. 17C) and resolving theimprovement changes within the buckets of the Nakanuma categoricalscores (FIG. 17B), according to an illustrative implementation;

FIG. 18 depicts a processed version of an exemplary digital image, inwhich certain regions of the digital image are recognized as collagen(gray pixels), and the collagen is organized into various collagenobjects of different collagen classes (indicated by different grayscaleintensities, according to an illustrative implementation; and

FIG. 19 depicts data indicating how the composite score, developed todistinguish between NASH 1 and NASH 2 patients, performs well when thescore is based on two top-performing parameters selected from a set ofcandidate parameters, according to an illustrative implementation.

DETAILED DESCRIPTION

To provide an overall understanding of the systems and methods describedherein, certain illustrative embodiments will now be described,including systems and methods for quantifying the phenotype of fibrosisfrom digital images of biological tissues. However, it will beunderstood by one of ordinary skill in the art that the systems andmethods described herein may be adapted and modified for other suitableapplications, such as any data classification application, and that suchother additions and modifications will not depart from the scopethereof. Generally, the computerized systems described herein maycomprise one or more engines, which include a processor or devices, suchas a computer, microprocessor, logic device or other device or processorthat is configured with hardware, firmware, and software to carry outone or more of the computerized methods described herein.

The present disclosure relates to systems and methods for quantifyingthe phenotype of fibrosis (corresponding to the fibrosis observabletraits) from a digital image of any tissue. The digital image reflectsan optical biomarker that is specific to the collagens present in thetissue. The systems and methods for quantifying the fibrosis phenotypemay be automated, and provide a continuous quantification of fibrosis inany kind of biological tissue from any digital image that depictscollagens in the biological tissue.

The digital image is generally an image of a biological tissue preparedand stained for collagen and digitally acquired by Whole Slide Imaging(WSI) methods. The present disclosure is not limited to any specifictissue imaging method, so long as the digital image includes adistinguishing marker specific to one or multiple collagen moleculesversus non-collagen molecules. The biological tissue could be from ahuman, animal or from any other biological system, and may be taken fromthe liver, lung, heart, muscle, skin, kidney, gut, uterus, eye, adiposetissue, tissue from the gastric intestinal tract, cancerous tissues inhumans or animals, or any other organ or portion of the body orphenotypically relevant biosystem that is susceptible to fibrosis. Thedigital image may include a two-dimensional digital image(s), athree-dimensional digital image(s), a digital image stack(s), a staticdigital image(s), a time-course series of digital images, a digitalmovie(s), or any suitable combination thereof. The digital imageincludes an optical marker specific to collagen, either fromcollagen-specific stains used in histopathology methods, or fromintrinsic bio-optical markers specific to collagen (and fibrosis)intrinsic to the optical imaging method (such as second harmonicgeneration) fibrosis, or in more general terms to any kind of collagen.

As used herein, an organ includes any of the aforementioned bodies orbody portions, or it may include meso-biological systems such asspheroids or printed organs that can also develop fibrotic conditions.Generally, an organ can include any phenotypically relevant biosystemthat is susceptible to fibrosis.

It is an object of the present disclosure to provide automated andcontinuous quantification of the fibrosis phenotype in any kind ofdigital image indicating the presence of one or more collagens from ofany kind of biological tissue. In contrast to a categorical approachthat only coarsely defines the stages of fibrosis as one of a smallnumber (e.g., 4-6) stages, an approach that uses continuousquantification uses a continuous scale to quantify the fibrosis. Thecontinuous scale allows for precise tracking of fibrosis progressioneven on short timescales, and allows for tracking of response totreatment or treatment candidates of fibrosis. It also allows for thesystem to distinguish and classify tissues with different phenotypes offibrosis (e.g., with possibly the same amount or basic features ofcollagen), and/or to develop biomarkers for fibrosis (as the continuousquantification of fibrosis can be used to establish robust correlationwith the continuous values of the biomarker candidates). The presentdisclosure provides a scoring system for fibrosis that is continuous,and offers improved signal-to-noise ratio, detection threshold anddynamic range so that it can precisely quantify the fibrosis phenotype.

The present disclosure has clinical applications in quantifying fibrosisof human patients, but also has pre-clinical applications in animal andother biological models. The fibrosis phenotypes correspond tofibrosis-related conditions having different outcomes of fibrosisdisease. In one example, the fibrosis phenotypes correspond to diseaseseverity, and may correspond to NASH-CRN F disease stages, which includeF0, F1, F2, F3, and F4. In one example, the fibrosis phenotypescorrespond to different values or ranges of a fibrosis-related biomarkerthat is indicative of progression (or regression) of fibrosis, severityof fibrosis, response to treatment, or any combination thereof. In oneexample, the fibrosis phenotypes correspond to different classes offibrosis, such as NASH 1 versus NASH 2 in pediatric populations withNASH. Any of these examples of fibrosis phenotypes may be present inhuman and animal biological tissues and accordingly have both clinicaland pre-clinical applications. In some implementations, a digital imageof a biological tissue sample is assessed for multiple fibrosisphenotypes at the same time. Specifically, different composite scoresmay be assessed on the same digital image, where each composite scorerepresents a different fibrosis phenotype. For example, for a patientsuffering from a fibrotic condition, a fibrosis severity compositescore, a fibrosis progression composite score, and a fibrosis typecomposite score may be assessed in parallel from the same digital imageof a biopsy to determine how severe the disease is, how fibrosis isprogression in the subject, and to classify the type of fibrosis in thesubject, respectively.

The figures of the present disclosure are described below in detail andillustrate exemplary systems, methods, data structures, and graphs thatprovide a robust and accurate way to quantitatively characterizefibrosis. The figures further demonstrate that the quantitativeapproaches described herein improve upon existing categorical approachesto quantify fibrosis severity by having a continuous scale that allows acomposite score to closely track progression of fibrosis as it worsens,and regression of fibrosis in response to treatment or therapy.

Specifically, FIGS. 1-2 depict a representative system 100 (FIG. 1 ) anda high level flow diagram 200 (FIG. 2 ) for implementing the systems andmethods of the present disclosure, for quantifying the phenotype offibrosis. FIGS. 3-9 show exemplary ways to extract various data fromdigital images taken of biological tissue samples having differingseverity of fibrosis as defined by pathologists and widely proven bypatient outcomes. Namely, the present disclosure relates to at leastthree levels of quantitatively characterizing fibrosis from an image. Asused herein, different levels of quantitatively characterizing fibrosisfrom an image correspond to different ways of characterizing theappearance and organization of collagens in the image. Specifically, thedifferent levels of the present disclosure relate to macroscopiccharacteristics of the collagens (tissue level), morphometriccharacteristics of the collagens (morphometric level), andorganizational characteristics of the collagens (texture level). Each ofthese levels is described in further detail below. FIGS. 3-4 relate totissue level features that describe macroscopic characteristics of thecollagens depicted in the digital image. FIGS. 5-7 relate tomorphometric level features that describe morphometric characteristicsof the collagens depicted in the digital image, and their relativehistogram distributions from which several quantitative parameters areextracted. FIGS. 8-9 relate to texture level features that describecollective and regional organizations and shapes of the collagensdepicted in the digital image, and their relative histogramdistributions from which several quantitative parameters are extracted.FIGS. 10-12 relate to an illustrative method and example histogram plotsfor selecting which of the various quantitative parameters representingthe features described in relation to FIGS. 3-9 to include in thegeneration of a composite score for representing the phenotype offibrosis on a continuous scale. FIGS. 13-14 and 18 depict exemplarydigital images that are pre-processed to identify collagen objects intissue samples. FIGS. 15-17 show that the phenotypic composite score ofthe present disclosure is able to track severity of fibrosis as well asregression of fibrosis, in response to treatment for instance, andimproves upon known coarse categorical approaches to defining thefibrosis phenotype. FIG. 19 indicate that the phenotypic composite scoreof the present disclosure is able to classify patients as NASH 1 versusNASH 2.

FIG. 1 depicts an exemplary system 100 for quantifying the phenotype offibrosis from a digital image containing collagen-specific informationin tissue, according to an illustrative implementation. The system 100includes a server 104 and a user device 108 connected over a network102. The server 104 and the user device 108 each include one or moreprocessors to perform any of the methods or functions described herein.As used herein, the term “processor” or “computing device” refers to oneor more computers, microprocessors, logic devices, servers, or otherdevices configured with hardware, firmware, and software to carry outone or more of the computerized techniques described herein. Processorsand processing devices may also include one or more memory devices forstoring inputs, outputs, and data that is currently being processed.

The user device 108 may include, without limitation, any suitablecombination of one or more devices configured with hardware, firmware,and software to carry out one or more of the computerized techniquesdescribed herein. Examples of user devices include, without limitation,personal computers, laptops, and mobile devices (such as smartphones,blackberries. PDAs, tablet computers, etc.). The user device 108includes a user interface, that includes, without limitation, anysuitable combination of one or more input devices (e.g., keypads, touchscreens, trackballs, voice recognition systems, etc.) and/or one or moreoutput devices (e.g., visual displays, speakers, tactile displays,printing devices, etc.). While only one server and one user device areshown in FIG. 1 to avoid complicating the drawing, the system 100 cansupport multiple servers and multiple user devices.

In some implementations, the user device 108 is a mobile device such asa smartphone or tablet. In general, one benefit of the systems andmethods of the present disclosure is that they do not requireparticularly high resolution digital images. Even digital images withrelatively low resolution, such as those captured on a device such as asmartphone or tablet, are able to be used with the techniques of thepresent disclosure, to quantify the fibrosis phenotype in a subject. Forexample, the user device 108 may be capable of receiving a small plug-indevice having a receptacle or a slot that holds a glass slide, such as amicroscope slide containing the biological tissue sample, in place. Theplug-in device may be configured to ensure uniform lighting for theslide, so that a camera on the user device 108 adequately captures thecollagens in the tissue sample in a reproducible manner.

The components of the system 100 are depicted as being connected over anetwork 102. The arrangement and numbers of components shown in FIG. 1are merely illustrative, and any suitable configuration may be used. Forexample, although FIG. 1 depicts system 100 as a network-based systemfor quantifying the phenotype of fibrosis, the functional components ofthe system 100 may be implemented as one or more components includedwith or local to the user device 108, that includes a processor, a userinterface, and an electronic database. The processor of the user device108 may be configured to perform any or all of the functions of theprocessors of the server 102. The electronic database of the user device108 may be configured to store any or all of the data stored in database106 of FIG. 1 . Additionally, the functions performed by the componentsof the system 100 may be rearranged. For example, the server 104 mayperform some or all of the functions of the user device 108, and viceversa.

The database 106 is a distributed system of databases that includes a“calibration data set” database 106 a, candidate parameter databasesincluding “tissue level parameters” database 106 b, “morphometric levelparameters” database 106 c, “texture level parameters” database 106 d,“collagen classes” database 106 e, and “selected parameters” database106 f. As depicted in FIG. 1 , each database is a separate database, butany of the databases shown in FIG. 1 may be combined into a commondatabase. For example, the candidate parameter databases 106 b-d may becombined together into a single database, which may further be combinedwith the collagen classes database 106 e, the calibration data setdatabase 106 a, or both.

The calibration data set database 106 a includes a set of calibrationdigital images taken from biological samples having known phenotypes offibrosis, which may be stored in metadata corresponding to the digitalimages. Multiple sets of calibration digital images may be stored in thecalibration data set database 106 a, where the different sets of imagescorrespond to different fibrosis phenotypes, such as severity offibrosis, type of fibrosis, or different stages of progression orregression of fibrosis. The digital image is generally an image of abiological tissue prepared and stained for collagen and digitallyacquired by Whole Slide Imaging (WSI) methods. The biological tissuecould be from a human, animal or from any other biological system, andmay be taken from the liver, lung, heart, muscle, skin, kidney, gut,uterus, eye, adipose tissue, tissue from the gastric intestinal tract,cancerous tissues in human or animals, or any other organ or portion ofthe body or phenotypically relevant biosystem that is susceptible tofibrosis.

Each calibration digital image is accompanied by metadata associatedwith the image, such as data regarding the patient or animal (e.g.,species, age, gender, genotype, race, blood or genetic biomarker data.),the nature, characteristics, locations of the collagen biomarker (e.g.Second Harmonic Generation channel, or type and color of thehistopathology collagen specific marker), image annotations on the image(e.g., regions of the image to exclude from the analysis), the fibrosisphenotype or severity stage of fibrosis as assessed by a physician,pathologist, or any other expert, body region or organ that is depictedin the image, whether the patient or animal has been treated forfibrosis or another medical condition and the corresponding treatmentprotocol, or any other suitable patient data), and/or data regarding theimage (e.g., when the image was taken, the imaging modality, imagedimensions, or any other suitable image data).

The set of calibration digital images stored in the calibration data setdatabase 106 a may include multiple subsets of calibration digitalimages, corresponding to different patient or animal populations, whichmay be further grouped according to specific sub-populations. Thesub-populations may include control groups having no disease and one ormore disease states, and test groups having variable treatment plans,such as different dosages of a drug or therapy. In an example, thesub-populations correspond to wild-type and genetically modified animalmodels such as knock-out animals, so that the present disclosure can beused to study the physiological mechanisms of fibrosis, its progression,and response to treatment, or to identify and quantify traits offibrosis that are different from such two genetic models, or toestablish a scoring technique that can be used to classify such twomodels in a blinded way.

The candidate parameter databases 106 b-d include candidate parameters,each of which is a quantitative measurement that characterizes thecalibration digital images in the calibration data set 106 a. Thecandidate parameters and examples of how they are derived fromindividual digital images are described in detail below, with referenceto FIGS. 3-9 . Briefly, the candidate parameters quantitatively assessthe digital images at three different levels. Specifically, for thecollagens depicted in a digital image, tissue level parameters quantifymacroscopic characteristics of the collagens, morphometric levelparameters quantify morphometric (e.g., shape and size) characteristicsof the collagens, and texture level parameters quantify an organizationof the collagens. While parameters at any one level may sometimes besufficient for characterizing the fibrosis phenotype in a subject,combining parameters from different levels (e.g., two or three levels)generally results in a robust and accurate method to quantify phenotypeof fibrosis.

The collagen classes database 106 e represent various classes ofcollagen that may be depicted in the calibration digital images. Thecollagen class corresponds to a specific type of collagen that isdepicted in the image, which may exhibit differently for differentfibrosis phenotypes. Thus, using quantitative parameters (e.g., any ofthe candidate parameters described in relation to 106 b-d) that arespecific to one or more particular collagen classes may further improverobustness and accuracy of the present disclosure. In fact, any of thecandidate parameters in databases 106 b-d may reflect total collagendepicted in an image, one specific collagen class depicted in the image,or multiple, but not all, collagen classes depicted in the image.

Example collagen classes include single collagen fibers, bundles ofcollagen fibers, diffuse collagen tissue regions, fine collagen,assembled collagen, tissue regions, long collagens, short collagens,high textured regions, low textured regions, highly complex collagenskeleton, less complex collagen skeleton, any other suitable type ofcollagen, or any combination thereof. FIGS. 13, 14, and 18 depictexample representations of how the system identifies different collagenobjects in an image, and how the system distinguishes between differentcollagen classes such as fine collagen and assembled collagen,respectively. Specifically, the system may perform image processing onthe raw digital image to identify certain regions of the digital imageas being representative of collagen or a specific form of collagen. Inother words, the digital image may be masked to identify specificregions as collagen. Different collagen classes may be useful fordistinguishing different fibrosis phenotypes. For example, quantitativeparameters for fine collagen may be used for distinguishing less severefibrosis stages (e.g., F0, F1, F2) while quantitative parameters forassembled collagen may be used for distinguishing more severe fibrosisstages (e.g., F2, F3, F4).

The selected parameters database 106 f corresponds to the set ofquantitative parameters from databases 106 b-d (and optionally 106 e)that are selected for inclusion in a composite score that quantifiesphenotype of fibrosis. An example process for selecting the parametersin database 106 f from the candidate parameters and calibration data setis described in detail in relation to FIGS. 10-12 . Briefly, theselected parameters correspond to those that distinguish betweenfibrosis phenotypes for a target subject population, that may reflect aspecific species, age, race, gender, disease state, any other suitablecharacteristics, or a combination thereof. Specifically, the selectedparameters may be those that, for the digital images in the calibrationdata set corresponding to the relevant target subject population, areable to distinguish across different fibrosis phenotypes withoutintroducing a large amount of noise.

Generally, the selected parameters stored in the selected parametersdatabase 106 f may be different for different objectives. For example, afirst set of parameters are selected for characterizing disease severity(e.g., to derive a composite score that provides a continuous scale forF0-F4). A second set of parameters may be selected for characterizingprogression of fibrosis. A third set of parameters may be selected forcharacterizing regression of fibrosis in response to treatment, such asa drug. A fourth set of parameters may be selected for classifying thetype of fibrosis (e.g., to derive a composite score that provides acontinuous scale and is thresholded by a cut-off value to classify thesubject as a type of fibrosis, such as NASH 1 versus NASH 2). Any of theparameters selected for the first, second, third, and fourth sets mayoverlap with one another, but generally, each of the described sets ofparameters may include one or more parameters that are unique to thatset, or that are not included in every other set. Each set of selectedparameters is used to compute a different composite score, with aspecific objective. In this manner, for a single digital image of abiological tissue sample, different sets of selected parameters may beapplied to derive different composite scores, to reflect multipleobjectives (e.g., characterizing the disease severity and type offibrosis) at the same time.

Moreover, the selected parameters stored in the selected parametersdatabase 106 f may be specific to a certain set of calibration digitalimages that share similar metadata, such as patients of a specificpopulation (e.g., age, race, gender, symptoms, blood test data), anddifferent sets of selected parameters may be applicable to differentpopulations.

In one example, as is described in more detail in relation to FIGS.10-12 , to determine whether a particular parameter can distinguishacross different fibrosis phenotypes without introducing much noise, theparameter's distribution of values (across the relevant calibrationdigital images for a specific fibrosis phenotype) is evaluated for itsmean and standard deviation, and the mean and standard deviation areevaluated for different fibrosis phenotypes. If the mean value changesfor different fibrosis phenotypes, and the standard deviation for theindividual phenotypes is not too large, then the parameter is selected.As is described in more detail below, the selected parameters arecombined to provide one or more composite scores that quantify thefibrosis phenotype of the human or animal in the digital image. Itshould be understood that the techniques and examples described inrelation to FIGS. 10-12 are shown for illustrative purposes only, othermethods of selecting informative parameters from a set of candidateparameters may be used without departing from the scope of the presentdisclosure.

In an implementation, the user device 108 provides a digital image takenof a histopathology tissue section, to the server 104 over the network102. The digital image may include a two-dimensional digital image(s), athree-dimensional digital image(s), a digital image stack(s), a staticdigital image(s), a time-course series of digital images, a digitalmovie(s), or any suitable combination thereof. The digital imageincludes an optical marker specific to collagen, either fromcollagen-specific stains used in histopathology methods, or fromintrinsic bio-optical markers specific to collagen (and fibrosis)intrinsic to the optical imaging method (such as second harmonicgeneration) fibrosis, or in more general terms to any kind of collagen.Unlike the digital images in the calibration data set database 106 a,the digital image from the user device 108 may not be associated with aknown phenotype of fibrosis. Alternatively, the fibrosis phenotype maybe known, or already assessed by a pathologist or clinician, but thepresent disclosure is used to validate that assessed phenotype.Generally, other metadata may accompany the digital image, such as anyof the other metadata described in relation to database 106 a. Thatmetadata may be used to determine which parameters to include in thecomposite score calculation. For example, the metadata may inform whichsubset of calibration digital images should be applied, and thereforewhich selected parameters to use to compute the composite score.

The server 104 performs a computational analysis on the digital image toobtain a score (or scores) that quantify the entire fibrosis phenotype,or a subset of the phenotype. That score can be used to describe theseverity, progression, regression, or type of the fibrosis in the tissuesample. To obtain the score(s), the server 104 performs the method 200described in relation to FIG. 2 , which is based on a set ofquantitative parameters that are described in relation to FIGS. 3-9 ,which are selected based on a calibration technique, such as the onedescribed in relation to FIGS. 10-12 .

In some implementations, the server 104 computes only a single scorethat characterizes the fibrosis in the digital image received from theuser device 108. Alternatively, the server 104 computes multiplecomposite scores, each of which characterizes the fibrosis in thedigital image. For example, different composite scores may be computedfor the same digital image, including one composite score thatquantifies the disease severity (e.g., F0-F4), optionally anothercomposite score that quantifies the progression of fibrosis, optionallyanother composite score that quantifies the regression of fibrosis inresponse to treatment, and optionally another composite score thatquantifies the type of fibrosis (e.g., NASH 1 versus NASH 2). For eachcomposite score calculation, different quantitative parameters areselected to be specific to the objective (e.g., to quantify severity,progression, regression, or type of fibrosis), as is discussed in moredetail in relation to FIG. 10 .

In some implementations, different composite scores are computed fordifferent levels of features. Specifically, a separate composite scoremay be computed for tissue level parameters, morphometric levelparameters, and texture level parameters, or for any combination of twolevels (e.g., tissue and morphometric, morphometric and texture, ortissue and texture). Then, the resulting composite scores may becombined into a single value, or combined as a vector to represent thefibrosis phenotype. In some implementations, using different compositescores for different levels of features is one way to remove potentialbiases that may be introduced as a result of one level havingsignificantly more selected parameters than another level. Generally,the composite scores may be normalized by the number of selectedparameters in a particular group, such as the level.

The server 104 provides the composite score(s) over the network 102 tothe user device 108, which may display the composite score(s) or anindication of the composite score(s) to a user. For example, the userdevice 108 may display the actual number or numbers corresponding to thecomposite score, which represents the fibrosis phenotype, such asdisease severity, progression or regression of fibrosis, or type offibrosis for the subject associated with the uploaded digital image.Similarly, the server 104 may provide a processed version of the digitalimage to the user device 108 for display. The processed version of thedigital image may indicate specific regions of the digital image thatare identified as certain classes of collagen, such as that depicted inFIG. 14 , or certain collagen objects, such as that depicted in FIG. 13.

Moreover, the server 104 may save the composite score(s), the originaldigital image, any metadata associated with the digital image (e.g.,patient information such as race, gender, age, blood test data, or imageinformation such as imaging modality, resolution, when the sample orimage was taken) and any indication of the extracted features of thedigital image to produce the composite score(s) into database 106, orany other suitable database accessible to the server 104. Saved data mayfurther include the parameters calculated from the digital image, andany processed versions of the digital image, such as those that indicatelocations of specific collagen objects (FIG. 13 ) or certain collagenclasses (FIG. 14 ).

FIG. 2 is a flowchart of a method 200 that may be implemented by thesystem 100 to quantify the phenotype of fibrosis from ahistopathological digital image of tissue. In general, the method 200provides an analysis that precisely quantifies the phenotype of fibrosisin the tissue sample. While the steps of the method 200 are describedbelow as being performed by the server 104, it will be understood thatthe user device 108 may perform any of all of the steps of the method200 locally, or any or all of the steps of the method 200 may beperformed by some other device, without departing from the scope of thepresent disclosure.

There are various ways to quantify the phenotype of fibrosis in thetissue sample, without departing from the scope of the presentdisclosure. One primary purpose of the approach described herein is toreduce the dimension of the data set (e.g., the set of candidateparameters), to identify and select parameters that account for thevariance in the calibration digital images (e.g., the variation in theway collagen appears in digital images for different fibrosisphenotypes). The selected parameters may be referred to herein as“principal parameters,” which are identified based on the computationalmethods described herein, to account for the changes in appearance ofcollagen for different types of fibrosis, different severities offibrosis, and different stages of progression or regression of fibrosis.The methods described in relation to FIG. 2 are provided as examplesonly, and it will be understood that other methods may also be used toidentify the principal parameters, such as principal component analysis(PCA).

At step 220, the server 104 receives a digital image of a biologicaltissue sample, wherein the digital image includes a depiction ofcollagens in the biological tissue sample. The present disclosure is notlimited to any specific tissue imaging method, so long as the digitalimage includes a distinguishing marker specific to one or multiplecollagen molecules versus non-collagen molecules. Notably, the systemsand methods of the present disclosure may apply the same computationalanalysis to quantify the fibrosis phenotype of the tissue sample,regardless of the specific tissue type, histology staining method,collagen biomarker, collagen optical biomarker or imaging modality thatwas used to create the digital image.

Generally, the imaging method to take the digital image of thebiological tissue sample involves an optical marker specific tocollagen. That optical marker may be from collagen-specific stains usedin histopathology methods, from intrinsic bio-optical markers specificto collagens (and fibrosis) intrinsic to the optical imaging method(such as second harmonic generation) fibrosis, or in more general termsto any kind of collagen.

The server 104 performs some pre-processing of the digital image whenthe digital image is received. Such pre-processing may includeidentification of the collagens that appear in the digital image,examples of which are depicted in FIGS. 13-14 and 18 . For instance,FIG. 13 depicts an example of detection of a collagen object in adigital image, from a set of adjacent pixels that represent an amount ofcollagen. In another example, FIG. 14 depicts a pre-processed digitalimage in which different collagen classes are identified, includingsteatosis, fine collagen, and assembled collagen. FIG. 18 depictsanother example of a raw, unprocessed digital image (top row) and theprocessed version (bottom row), depicting identification of differentcollagen objects of different types, indicated by gray-scale intensity.For example, such pre-processing may include color-based segmentation,thresholding, filtering, enhancement, texture analysis, binarization,edge detection, region analysis, Fourier transformation, objectdetection, object analysis segmentation, skeletonization, machinelearning, deep learning for image processing, 2D and 3D variants of anyof these techniques, and any other computational technique that canenrich the extraction of collagens from an image.

In some implementations, the image is taken using a tissue imagingmethod or combination of tissue imaging methods or modalities that canenrich the detection signal of the fibrous tissue, so that the collagenin the resulting image of the tissue sample is more easily detected.Suitable tissue imaging methods include fluorescent imaging, usingex-vivo fresh tissue, performing optical biopsies, in-vivo imaging (suchas endoscopic imaging, for example). In general, any digital imaging andoptical methods may be used, including stained histopathology slidesimaged by Whole Slide Imaging Scanners, two-photon microscopy,fluorescence imaging, structured imaging, polarized imaging, CARS, OCTimages, and other images of biological tissue, or any suitablecombination thereof, in any configuration and wavelength. The presentdisclosure is applicable to any kind of digital imaging methods thatwould generate a digital image by virtual biopsy imaging, such as isused for fresh tissue imaging and/or endoscopy.

At step 222, the server 104 processes the image received at step 220, toquantify a plurality of parameters, each parameter describing a featureof the collagens in the biological tissue sample that is expected to bedifferent for different phenotypes of fibrosis. The quantitativeparameters represent various features of collagens appearing in thedigital image, either individually or as a group, and are the sameparameters described above, selected from the set of candidateparameters stored in databases 106 b-d and described below in detail inrelation to FIGS. 3-9 . The quantitative parameters are generally sortedinto three levels and are described in detail in relation to FIGS. 3-9 .

As discussed briefly above, these quantitative parameters can becategorized into at least three distinct levels: (1) the tissue level,in which collagen is measured macroscopically as an aggregation ofcollagens across the image; (2) the morphometric level, which quantifiesthe shape and size of individual collagens (or collagen objects); and(3) the texture level, which quantifies the organization of thecollagens with respect to one another across the image.

FIGS. 3-9 depict how different types of parameters are used to quantifyfibrosis in digital image. Each of FIGS. 3, 5-6, and 8 depicts a set ofthree exemplary digital images of tissue samples (included in thecalibration data set, for example) having different known fibrosisphenotypes (e.g., F0, F2, and F4). The same three digital images arerepeated in each of FIGS. 3, 5-6, and 8 . Below each image in each ofthese figures is a table that lists example quantitative parameters thatrepresent tissue level features (FIG. 3 ), morphometric level features(FIGS. 5-6 ), or texture level features (FIG. 8 ) for each fibrosisphenotype of F0, F2, and F4.

Moreover, each of FIGS. 4, 7, and 9 show data structures that summarizethe tissue level features (FIG. 4 ), morphometric level features (FIG. 7), and texture level features (FIG. 9 ) and indicate whether eachparameter is selected to be included in the generation of the compositescore. For FIGS. 5-9 (morphometric level and texture level), thequantitative parameters are based on histogram analysis, as is describedin detail below. The figures only indicate exemplary quantitativeparameters, and are for illustrative purposes only. For simplicity ofthe figures, not all possible parameters are shown, and it should beunderstood that different parameters could be used without departingfrom the scope of the present disclosure. The appendix includes anotherexemplary list of quantitative parameters that may be used, butdifferent parameters could also be included, such as different metricsand statistics, including those of different cut-off values thanindicated.

Moreover, each quantitative parameter described herein may berepresentative of the collagen as a whole (e.g., total collagenrepresented in the digital image), or may otherwise only represent aportion of the depicted collagen. For example, the parameters may becomputed only on specific area of the tissue (e.g., border, portalregion, around glomeruli in the kidney, or any other suitable specificarea), in subgroups or classes of the collagen, such as large versussmall collagen (as defined by the form factors of their skeleton), faintversus dense collagen (as defined by the average optical/pixel/intensityvalue), individual versus bundles of collagen fibers, fine versusassembled collagen (see FIG. 14 ), other suitable classes of collagen,or any suitable combination thereof. Limiting certain quantitativeparameters to specific areas or subgroups of the total collagen depictedin the digital image may lead to more precision for quantifying fibrosisphenotype in the final scoring system.

As discussed below, some parameters relate to a single valuecharacterization of the collagen depicted in the digital image (e.g.,tissue level parameters). Other parameters (e.g., morphometric levelparameters and/or texture level parameters) relate to multiple valuecharacterization, and involve statistics (e.g., computed fromhistograms) to account for trends (e.g., mean), transition (e.g.,statistics limited to above or below cut-off values, or ranges inbetween cut-off values), or variability (e.g., standard deviation,kurtosis). These examples are provided for illustrative purposes only,and in some implementations, tissue level parameters are associated withhistograms and involve statistics. Moreover, in some implementations,morphometric level parameters and/or texture level parameters relate tosingle value characterization. The cut-off values segment a distributioninto multiple sections, so as to create different statistics for thesections. For example, long versus short collagen fibers may be definedby a cut-off value separating long versus short. Similarly, anothercut-off value may define low versus high texture entropy. The selectionof cut-off values may be performed by a user, and optimized for specifictissues. For example, a low cut-off value for length may be used forrodents, while a higher cut-off value for length may be used for largeranimals. As another example, the user may select the cut-off valuedepending on the phenotyping objective, such as detection of a fibrosisphenotype with low severity (e.g., F0, F1, F2) compared to higherseverity (e.g., F2, F3, F4).

At the tissue level (FIGS. 3-4 ), these features describe themacroscopic properties of the collagens in the digital image andquantify overall amounts of collagen depicted across the entire digitalimage. Example tissue level parameters include the object normalizedcount, skeleton nodes normalized count, collagen reticulation index,total collagen area ratio, the large collagen object normalized density(count per area of surface), the small collagen object normalizeddensity, and others. As depicted in FIG. 4 , some parameters correspondto transformations of one or more other parameters. For example, totalcollagen area ratio is one parameter listed in FIG. 4 , and so is itssquare root. Similarly, the assembled/fine CAR ratio is a parameter thatcorresponds to a transformation of two other parameters: the assembledCAR and fine CAR. These transformations are included as examples only,and in general, any transformation may be applied to any combination ofthe parameters, including parameters at the tissue, morphometric, andtexture levels.

At the morphometric level (FIGS. 5-7 ), these features describe themorphometric characteristics (or the shape and dimensions) of thecollagens in the digital image. Because there is generally more than asingle collagen (e.g., one fiber) depicted in the digital image, and insevere cases, that could be many collagens or collagen objects (e.g.,multiple fibers, bundles of fibers, or tissue regions) depicted in theimage. Rather than including a different value characterizing eachindividual collagen fiber as a parameter, the approach described hereinuses a histogram analysis to analyze the distribution of morphometricvalues across the collagens or collagen objects appearing in the image.In this manner, the quantitative parameters describing morphometriclevel features correspond to statistics that can be derived from thehistogram (or distribution of values), such as normalized count, mean,median, standard deviation, skew, kurtosis, and any other suitablestatistic. Example morphometric characteristics that are used togenerate the histograms include length, skeleton length, width,eccentricity, solidity, curvature ration, area, perimeter, collagendensity, color intensity, form factors such as area to perimeter ratio,or color to curvature ratio, and any other relevant parameter thatdescribes the shape, dimensions, or appearance of collagens.

At the texture level (FIGS. 8-9 ), these features describe theorganization, or distribution, of the collagens in the digital image. Inorder to capture how the collagens are distributed throughout the image,the approach described herein uses a histogram analysis, similar to themorphometric level analysis described above in relation to FIGS. 5-7 .However, rather than analyzing the distribution of values acrosscollagens or collagen objects (as is done for morphometric levelfeatures), the texture level features involve a distribution of valuesacross different regions of the digital image. For example, a samplevalue may be measured from a sample window (having size smaller than thesize of the overall digital image) of the digital image. A set of samplevalues are derived as the sample window is shifted (in an overlapping ornon-overlapping manner) in both dimensions (x- and y-directions) acrossthe digital image. That set of sample values corresponds to a sampledistribution for the texture level analysis, which generates a histogramof the sample values for a digital image. In this manner, thequantitative parameters describing texture level features correspond tostatistics that can be derived from the histogram (or distribution ofvalues), such as normalized count, mean, medium, standard deviation,skew, kurtosis, and any other suitable statistic. Example texturecharacteristics that are used to generate the histograms include secondorder statistics including the collagen image pixel intensity levelco-occurrence matrix and subsequent calculation of parameters such asenergy, homogeneity, correlation, inertia, entropy, skewness, kurtosis,related GLCM parameters, and any other relevant parameter that describesthe organization of collagens in an image.

Returning to FIG. 2 , at step 224, the server 104 combines the pluralityof parameters quantified at step 222, to obtain one or more compositescores indicative of a phenotype of fibrosis for the biological tissuesample. The composite score may be derived based on a mathematicaltransfer function that combines some or all of the quantitativeparameters computed at step 222, such as a sum of the selectedquantitative parameters, where the sum can be a normalized sum or aweighted sum. The composite score precisely quantifies the fibrosisphenotype (generally along a continuous scale so that it improves uponthe coarse, categorical approaches of the prior art by providing widedynamic range and high resolution), and can be used to describe thestate of fibrosis in the biological tissue sample, progression offibrosis in the sample, or regression of fibrosis in the sample inresponse to treatment. Derivation of the method to compute the compositescore may involve manual and/or automated methods that reduce thedimension of the calibration data set (by identifying candidateparameters that have the best signal-to-noise and are validated byexisting models of fibrosis (such as METAVIR), as described below withreference to FIGS. 10-12 ), identify correlations and/or principalcomponents, or any combination thereof.

In general, multiple composite scores may be computed, where eachcomposite score is specific to a particular level (e.g., tissue,morphometric, or texture) and/or collagen class (e.g., fine orassembled). For example, a Tissue-Level Fibrosis Composite Score, aMorphometric-Level Composite Score, a Texture-Level Composite Score,and/or a phenotypic composite score may be calculated. Then, theresulting composite scores may be combined to obtain a single value forquantifying the fibrosis phenotype. Alternatively, the multiplecomposite scores may remain separate as a vector of a small set ofnumbers that describe the fibrosis phenotype. The composite score may bereferred to herein as the Fibrosis Composite Score.

The following description of FIGS. 10-12 provide ways to select specificparameters from the list of candidate quantifiable parameters, forinclusion in the computation of the composite score.

FIG. 10 depicts an exemplary flow diagram of a calibration process 1000for selecting parameters to include in a calculation of a compositescore for quantifying a phenotype for fibrosis for a specificpopulation. The process 1000 is discussed below as being performed bythe server 104, but in general, may be performed locally by the userdevice 108 or by any other suitable device that can receive the digitalimage from the user device 108, either over the network 102 otherwise.

As discussed above in relation to the selected parameters database 106f, the calibration process 1000 selects parameters from a set ofcandidate parameters that are computed based on a set (or a relevantsubset) of calibration digital images. The selected parameters areincluded in the calculation of a composite score that quantifiesphenotype of fibrosis, so the selected parameters should distinguishbetween fibrosis phenotypes for a specific target subject population(e.g., corresponding to the subject of the digital image that isuploaded by the user device 108, for example). Generally, the selectedparameters are those that, for the digital images in the calibrationdata set corresponding to the relevant target subject population, areable to distinguish across different fibrosis phenotypes withoutintroducing a large amount of noise.

At step 1032, the server 104 receives the calibration digital images1030 (described in relation to the calibration data set database 106 a),and separates the calibration digital images 1030 according to fibrosisphenotype. As discussed above, the fibrosis phenotypes correspond tofibrosis-related conditions having different outcomes of fibrosisdisease. Those different outcomes may correspond to different diseaseseverity (e.g., NASH-CRN F disease stages, which include F0, F1, F2, F3,and F4), different values or ranges of a fibrosis-related biomarker thatis indicative of progression of fibrosis, regression of fibrosis inresponse to treatment, or both, or different classes of fibrosis (e.g.,NASH 1 versus NASH 2). For the calibration data set, the fibrosisphenotypes of the digital images are known, and are as evaluated by aphysician or pathologist (e.g., F0, F1, F2, F3, F4, NASH 1, NASH 2,etc.). The calibration digital images 1030 are separated into differentcategories according to their corresponding known fibrosis phenotypes.

The calibration digital images 1030 may be further separated accordingto other metadata associated with the images 1030. For example, thecalibration digital images 1030 may be separated according to specificpopulation data, such as race, gender, age, organ, or any other knowndata associated with the calibration digital images 1030.

At step 1034, a fibrosis phenotype iterative parameter i is initializedto one. At step 1042, the server 104 receives three sets of candidateparameters (e.g., candidate tissue level parameters 1036, candidatemorphometric level parameters 1038, and candidate texture levelparameters 1040) are received, and for the i-th fibrosis phenotype,processes the corresponding calibration digital images 1030 to obtainmean and standard deviation of each candidate parameter.

When the digital images are evaluated for the quantitative parameters,all of the image may be processed, or just some of the image may beprocessed, such as regions of the image corresponding to a specifictarget location of the biological tissue sample. For example, when thebiological tissue sample is of the liver, the collagens in the image maybe located in the septal region, the portal region, the peri-vascularregion, the collagen capsule, or structural collagen regions. Any ofthese regions or a combination thereof may be included for assessment.An understanding of these regions may also inform the selection ofcandidate parameters. For example, anatomically relevant collagens mayinclude septal bridges in liver or glomeruli in kidney, that haveexpected sizes, shapes, and arrangements. Moreover, the digital imagesmay be preprocessed to identify certain collagen objects (see FIG. 13 )or to identify certain collagen classes (see FIG. 14 ).

As discussed above, some of the candidate parameters 1036, 1038, and1040 may correspond to the total collagens in the image, or a subset ofthe collagens, such as those of a particular collagen class or classes.If the i-th fibrosis phenotype is not the last fibrosis phenotype to beconsidered (decision block 1044), the iterative parameter i isincremented (step 1046) and the process 1000 returns to block 1042 toevaluate the mean and standard deviation of each candidate parameter forthe next fibrosis phenotype. This process is repeated until all fibrosisphenotypes are considered (e.g., the i-th fibrosis phenotype is the lastfibrosis phenotype.

In other words, for each fibrosis phenotype i, the correspondingcalibration digital images 1030 are identified (having known fibrosisphenotypes corresponding to the i-th fibrosis phenotype). For each ofthose identified images, the set of candidate parameters across thethree levels (1036, 1038, and 1040) are evaluated, to generate an N×Mmatrix, where N corresponds to the total number of candidate parameters,and M corresponds to the number of calibration digital images for thei-th fibrosis phenotype. For each candidate parameter, the mean andstandard deviation of the M corresponding values are evaluated.

When all fibrosis phenotypes have been evaluated for mean and standarddeviation, the process 1000 then evaluates the means and standarddeviations of each candidate parameter to determine whether therespective candidate parameter has a signal (e.g., distinguishes betweendifferent fibrosis phenotypes of the calibration data set) withoutintroducing much noise (e.g., low standard deviation within a givenfibrosis phenotype of the calibration data set). To begin, the server104 proceeds to step 1048 to initialize a candidate parameter j to one.

At step 1050, for the j-th candidate parameter, the server 104 assessesthe rate of change of the mean for different fibrosis phenotypes, todetermine whether the j-th candidate parameter distinguishes betweendifferent fibrosis phenotypes. The rate of change may be assessed acrossdifferent types of fibrosis so as to determine whether the j-thcandidate parameter can distinguish between types, or across differentstages of fibrosis progression, so as to determine whether the j-thcandidate parameter can distinguish between different severities offibrosis.

At step 1052, the server 104 determines whether the rate of changeevaluated at step 1050 is above a first threshold, to determine whetherthe j-th candidate parameter distinguishes between fibrosis phenotypes.If so, the server 104 proceeds to step 1054 to determine whether thestandard deviation for the j-th candidate parameter is below a secondthreshold. If so, the server 104 proceeds to step 1056 to add the j-thcandidate parameter to a set of selected parameters. Then, if the j-thparameter is not the last candidate parameter (decision block 1058),then the iterative candidate parameter j is incremented (step 1062), andthe server 104 returns to block 1050 to consider the next j-th candidateparameter. For any j-th parameter for which the rate of change is below(or equal to) the first threshold (decision block 1052) or the standarddeviation is above (or equal to) the second threshold (decision block1052), the server 104 skips step 1056, does not add the j-th parameterto the set of selected parameters, and proceeds to the next parameter.These steps (1050, 1052, 1054, 1056, 1058, and 1062) are repeated untilall M candidate parameters have been considered and are either selectedor not selected. At step 1060, the server 104 outputs the set ofselected parameters.

FIG. 11 depicts four plots of exemplary mean values of normalizedcandidate parameters (y-axes) for different fibrosis phenotypes (x-axes,representing fibrosis progression with increasing disease severity). Asdepicted in FIG. 11 , many candidate parameters do not change much fordifferent fibrosis phenotypes, and thus have relatively flat slopes.These candidate parameters with flat slopes (e.g., rate of change aroundzero) are generally not selected because they do not distinguish betweenthe different fibrosis phenotypes (decision block 1052).

Other candidate parameters exhibit moderate or large rates of changesand have positive, significant slopes. These candidates are more likelyto be selected because they correlate in a positive manner with thedifferent fibrosis phenotypes (e.g., they increase with increasingdisease severity or fibrosis progression). However, if these candidateparameters are associated with large standard deviations (decision block1054), in either one or more fibrosis phenotypes, then that candidateparameter is not selected because it would introduce undesirable noiseto the composite score calculation that could outweigh the benefit ofbeing able to distinguish between fibrosis phenotypes.

Lastly, still other candidate parameters exhibit negative rates ofchange and have negative, significant slopes. These candidates are alsolikely to be selected because even though they correlate in a negativemanner with the different fibrosis phenotypes (e.g., they decrease withincreasing disease severity or fibrosis progression), they stilldistinguish between fibrosis phenotypes.

In some embodiments, the present disclosure allows for differentparameters to be selected for distinguishing between different sets offibrosis phenotypes. For example, a first set of parameters may beselected to distinguish between F0 and F2, and a second set ofparameters (with potential for overlap with the first set of parameters)may be selected to distinguish between F2 and F4. In this case, thesystem may take an adaptive approach that generates a first compositescore for the first set of parameters, and a second composite score forthe second set of parameters, and then align the two composite scores(e.g., by adding an offset to one or both of the scores) so that theyhave the same value where they meet (i.e., for the F2 phenotype).Similarly, the system may align the derivatives of the two compositescores so that the slopes are the same for the F2 phenotype.

FIG. 12 depicts an exemplary plot showing the noise versus rate ofchange of various quantitative parameters in a calibration data set fora specific population, according to an illustrative implementation.Specifically, the x-axis of FIG. 12 corresponds to an absolute value ofthe rate of change of the mean of a candidate parameter across differentfibrosis phenotypes of the calibration data set, while the y-axiscorresponds to the noise of the candidate parameter (e.g., representedby standard deviation). Each dot corresponds to a different candidateparameter. The vertical line at a rate of change of about 2, correspondsto the first threshold (decision block 1052), and the horizontal linecorresponds to the second threshold (decision block 1054). According tothe process 1000 of FIG. 10 , the candidate parameters corresponding todots below and to the right of the threshold lines have lowsignal-to-noise ratios, and are selected for inclusion in computation ofthe composite score. The other candidate parameters above or to the leftof the threshold lines are not selected.

As depicted in FIGS. 10 and 12 , at decision blocks 1052 and 1054, therate of change and standard deviations are simply compared to first andsecond thresholds, respectively, but in general, more complexcalculations may be used. For example, for principal component analysis(PCA), an input matrix may be provided, that includes the parametervalues for the set of candidate parameters, for the different knownfibrosis phenotypes (e.g., F0-F4). The output of the PCA corresponds tothe parameters that account for the variation in collagen features forthe different fibrosis phenotypes.

The steps of the process 1000 are depicted in FIG. 10 in a particularorder, but it should be understood that the order of any step of theprocess 1000 is not necessarily dependent on a previous step. Forexample, any of the steps of process 1000 may be reversed, or performedin parallel with other steps, without departing from the scope of thepresent disclosure, as long as any steps that depend on other steps areperformed subsequent to those steps. Moreover, the process 1000 isdescribed as being performed by the server 104, but any of the steps,including all of them, could be performed locally on the user device 108or any other suitable device capable of receiving the digital imagedirectly or indirectly from the user device 108.

The present disclosure provides several advantages over knowncategorical approaches to phenotyping fibrosis. Several advantages areapplicable to patients. Specifically, for the patient having afibrosis-related condition, the present disclosure improves to improvethe evaluation and follow up of fibrotic disease conditions such as IPF,Inflammatory Bowel Disease (IBD), Hepatitis (A, B, or C), Chronic KidneyDisease, scleroderma, Macular degeneracies, NASH, Alcoholic SteatosisHepatitis (ASH), Cirrhosis, Primary Biliary Cholangitis and PrimarilyBiliary Cirrhosis, renal disease, scarring, Duchenne muscular dystrophy,myocardial infarction and repair, glaucoma uterine, all kinds ofmanifestation of fibrosis in cancers, among others by providing a robustand accurate way to evaluate both severity of fibrosis and phenotype offibrosis progression and presenting the patient's fibrosis phenotype ina simple score. The automated systems and methods disclosed herein alsoavoid inter-pathologist and intra-pathologist evaluation errors, furtherimproving its robustness and accuracy.

Moreover, the scoring systems and methods disclosed herein provide acontinuous scale that has a high detection threshold and wide dynamicrange to quantify fibrosis phenotype of the biological tissue withsensitivity and precision. Due to its very high signal-to-noise, thepresent disclosure is sensitive to even slight changes in fibrosisprogression in a patient, on shorter time scales than previously allowedwith coarse systems. In other words, the scoring systems and methodsdisclosed herein improve upon earlier fibrosis phenotyping approachesbecause they do not require waiting for long periods of time beforedetecting a change in the progression of fibrosis. The robustness of thefibrosis scoring systems and methods disclosed herein also improve theefficiency of development and approval of new therapeutics, by beingable to detect small changes in both progression and regression offibrosis in response to treatment.

The present disclosure also provides scoring systems and methods thatare specific to a particular fibrosis phenotype of the patient orexpressed in the image of the biological tissue. For example, thescoring systems and methods could distinguish between to pediatric NASHtype I versus pediatric NASH type II. Other fibrosis phenotypes mayexist, such as T2-diabetes induced NASH versus obesity induced NASH.

Other advantages are applicable to the physician and clinical team thatcares for the patients. For example, the present disclosure improves thepersonalized management of fibrotic disease therapeutic regimens, suchas treatments for IPF, Inflammatory Bowel Disease (IBD), Hepatitis (A,B, or C), Chronic Kidney Disease, scleroderma, Macular degeneracies,NASH, Alcoholic Steatosis Hepatitis (ASH), Cirrhosis, Primary BiliaryCholangitis and Primarily Biliary Cirrhosis, renal disease, scarring,Duchenne muscular dystrophy, myocardial infarction and repair, glaucomauterine, all kinds of manifestation of fibrosis in cancers, amongothers. By providing a robust, sensitive, accurate and reproducible wayto evaluate fibrosis severity and progression in a continuous way, thepresent disclosure allows improved monitoring of patients over the longrun, and even across different clinical teams.

Moreover, while other approaches are sometimes unable to distinguishbetween even the coarse categorical stages of fibrosis (e.g., such asthe intermediate stages of fibrosis, F2 and F3), the present disclosurehas no such issue. The present disclosure provides a robust, sensitive,accurate, and reproducible way to evaluate fibrosis, which allows for animproved monitoring of patients during clinical trials. The systems andmethods disclosed herein can also be fully automated, meaning thatthroughput is high, and a pathologist is not needed for scoring. Thepresent disclosure is also fully compatible with existing workflows inthe pathology labs, as images can be processed in real time.

The present disclosure may be used to identify biomarkers or othercharacteristics that correlate with fibrosis, that may not have beenotherwise identified. Specifically, the systems and methods of thepresent disclosure provide an efficient and robust way of analyzing andphenotyping digital images of tissue samples, such as those in thecalibration data set described above. If the calibration data setfurther includes metadata corresponding clinical data, such as bloodtest data, the present disclosure may be used to identify certainbiomarkers in the blood test data (or any other characteristic of thesubject, such as age, race, gender) that correlate with the severity,progression, regression, or type of fibrosis. For example, male andfemale subjects may exhibit fibrosis differently from one another, andmay respond to treatment differently. These differences can be assessedand characterized with the systems and methods of the presentdisclosure.

Other advantages are applicable to pharmaceutical companies that areresearching potential therapies to treat fibrosis-related conditions.For example, the present disclosure improves translational research andproduct launches by gathering comprehensive high quality data related toanimal and/or patient reaction to investigational compounds, henceincreasing the likelihood of successfully developing new anti-fibroticdrugs, and/or minimize or reduce the fibrosis-related side effects ofnew or existing drugs. Because the scoring systems and methods can beautomated, they are suitable for mid-throughput and even high-throughputworkflows, which are more likely to accelerate the discovery of newtherapeutic compounds. Moreover, the present disclosure is translationaland applies both to pre-clinical models and clinical, across all kindsof fibrotic diseases and oncology (stromae).

The continuous, robust, sensitive, accurate and reproducible scoringmethods and systems disclosed herein provide an efficient, quantitative,unbiased, and automated way to evaluate fibrosis during pre-clinicaltrials (see FIG. 15 ) and clinical trials (see FIG. 16 ). The presentdisclosure reduces the number of animals (or patients) required toobtain statistically relevant data. The systems and methods disclosedherein examine multiple quantitative parameters that describe fibrosisstages, progression, and regression, which can be used to support adetailed understanding of the effect of investigational compounds, andoptionally the mechanism of action of those compounds.

As an example of a pre-clinical, discovery application, FIG. 15 depictson the left, a heat map of a set of selected quantitative parameters(y-axis) describing collagen features in a calibration data set fordifferent populations, including control, untreated, vehicle, andtreated at different doses (10, 30, and 100 mg/kg), and on the right, abar graph showing the fibrosis composite score is able to track thedifferent stages of disease, including response to treatment, accordingto an illustrative implementation.

As an example of a clinical application, FIG. 16 depicts on top, a heatmap of a set of selected quantitative parameters (y-axis) describingcollagen features in a calibration data set for different populations,including F0, F1, F2, F3, and F4 (x-axis), and on the bottom, a bargraph showing the fibrosis composite score is able to track thedifferent stages of the disease, according to an illustrativeimplementation.

In another example of a clinical application, FIG. 19 depicts two heatmaps of a set of candidate quantitative parameters (y-axes) describingcollagen features in a calibration data set for different NASH 1 andNASH 2 populations, for F0, F1, and F2 patients. For two selectedparameters, the fibrosis composite score is able to correctly classifythe patients as NASH 1 versus NASH 2 about 85% of the time, according toan illustrative implementation.

Furthermore, because the present disclosure can be specific to thefibrosis phenotype of its patient: for instance pediatric NASH type 1 vspediatric NASH type 2, or T2-diabetes induced NASH versus obesityinduced NASH, the scoring systems and methods can be used to betterunderstand the response of the patient to treatment, or the lackthereof.

Moreover, the present disclosure may be developed into an automaticdiagnostic tool for patient, hence accelerating the access to importantpatient information while reducing the cost of the diagnostic. Thescoring systems and methods of the present disclosure are consistentwith existing methods of characterizing fibrosis (see FIG. 17 ), butimproves upon those categorical methods by providing a wide dynamicrange and fine resolution for quantifying fibrosis. As an example, FIG.17 depicts data indicating the fibrosis composite score correlates withthe Nakanuma fibrosis stages, and provides improved dynamic rangerelative to the Nakanuma system, according to an illustrativeimplementation.

While many of the examples in the present disclosure are described withreference to features of collagens in a digital image, the presentdisclosure may be also applied to detecting and characterizing otherfeatures of tissue. For example, NASH tissues tend to include fatvacuoles, and the systems and methods of the present disclosure may beused to characterize tissue features such as fat vacuoles.

The systems and methods of the present disclosure are described as usinga calibration data set to select quantitative parameters from a set ofcandidate parameters. In general, a machine learning technique may beused without departing from the scope of the present disclosure, thatanalyzes a training data set with user-defined feature classificationcriteria, to learn the optimal combination of image features that bestdistinguish between fibrosis phenotypes. For example, artificialintelligence and machine learning techniques may be applied to reducethe dimension of the set of candidate parameters, identify correlationsbetween parameters and fibrosis phenotypes, and identify principalcomponents to establish meaningful composite scores.

It is to be understood that while various illustrative implementationshave been described, the forgoing description is merely illustrative anddoes not limit the scope of the invention. While several examples havebeen provided in the present disclosure, it should be understood thatthe disclosed systems, components and methods may be embodied in manyother specific forms without departing from the scope of the presentdisclosure.

The examples disclosed can be implemented in combinations orsub-combinations with one or more other features described herein. Avariety of apparatus, systems and methods may be implemented based onthe disclosure and still fall within the scope of the invention. Also,the various features described or illustrated above may be combined orintegrated in other systems or certain features may be omitted, or notimplemented.

While various embodiments of the present disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the disclosure. It should beunderstood that various alternatives to the embodiments of thedisclosure described herein may be employed in practicing thedisclosure.

All references cited herein are incorporated by reference in theirentirety and made part of this application.

APPENDIX Example List of Candidate Parameters for Quantifying Fibrosis

-   -   Total Collagen Phenotype Normalized Counts    -   Fine Collagen Phenotype Normalized Counts    -   Assembled Collagen Phenotype Normalized Counts    -   Skeleton Nodes Normalized Count    -   Skeleton Branch Normalized Count    -   Collagen Reticulation Index    -   Collagen STRUCTURE Index    -   Total Collagen Area Ratio (%)    -   SQRT(Total Collagen Area ratio %)    -   Fine Collagen Area Ratio (%)    -   SQRT(Fine Collagen Area Ratio)    -   Assembled Collagen Area Ratio (%)    -   Collagen Fiber Density %    -   SQRT(Assembled/Fine CAR)    -   Assembled/    -   Fine CAR Ratio    -   Normalized count of Short for Length at 30 Cut Off for All        Collagen phenotypes    -   Normalized count of Long for Length at 30 Cut Off for All        Collagen phenotypes    -   mean for Length at 30 Cut Off for All Collagen phenotypes    -   median for Length at 30 Cut Off for All Collagen phenotypes    -   std for Length at 30 Cut Off for All Collagen phenotypes    -   skew for Length at 30 Cut Off for All Collagen phenotypes    -   kurtosis for Length at 30 Cut Off for All Collagen phenotypes    -   Normalized count of Short for Total Skeleton Length at 27 Cut        Off for All Collagen phenotypes    -   Normalized count of Long for Total Skeleton Length at 27 Cut Off        for All Collagen phenotypes    -   mean for Total Skeleton Length at 27 Cut Off for All Collagen        phenotypes    -   median for Total Skeleton Length at 27 Cut Off for All Collagen        phenotypes    -   std for Total Skeleton Length at 27 Cut Off for All Collagen        phenotypes    -   skew for Total Skeleton Length at 27 Cut Off for All Collagen        phenotypes    -   kurtosis for Total Skeleton Length at 27 Cut Off for All        Collagen phenotypes    -   Normalized count of Thin for Width at 5 Cut Off for All Collagen        phenotypes    -   Normalized count of Thick for Width at 5 Cut Off for All        Collagen phenotypes    -   mean for Width at 5 Cut Off for All Collagen phenotypes    -   median for Width at 5 Cut Off for All Collagen phenotypes    -   std for Width at 5 Cut Off for All Collagen phenotypes    -   skew for Width at 5 Cut Off for All Collagen phenotypes    -   kurtosis for Width at 5 Cut Off for All Collagen phenotypes    -   Normalized count of Short for Perimeter at 100 Cut Off for All        Collagen phenotypes    -   Normalized count of Long for Perimeter at 100 Cut Off for All        Collagen phenotypes    -   mean for Perimeter at 100 Cut Off for All Collagen phenotypes    -   median for Perimeter at 100 Cut Off for All Collagen phenotypes    -   std for Perimeter at 100 Cut Off for All Collagen phenotypes    -   skew for Perimeter at 100 Cut Off for All Collagen phenotypes    -   kurtosis for Perimeter at 100 Cut Off for All Collagen        phenotypes    -   Normalized count of Small for Area at 240 Cut Off for All        Collagen phenotypes    -   Normalized count of Large for Area at 240 Cut Off for All        Collagen phenotypes    -   mean for Area at 240 Cut Off for All Collagen phenotypes    -   median for Area at 240 Cut Off for All Collagen phenotypes    -   std for Area at 240 Cut Off for All Collagen phenotypes    -   skew for Area at 240 Cut Off for All Collagen phenotypes    -   kurtosis for Area at 240 Cut Off for All Collagen phenotypes    -   Normalized count of Small for Filled Area at 250 Cut Off for All        Collagen phenotypes    -   Normalized count of Large for Filled Area at 250 Cut Off for All        Collagen phenotypes    -   mean for Filled Area at 250 Cut Off for All Collagen phenotypes    -   median for Filled Area at 250 Cut Off for All Collagen        phenotypes    -   std for Filled Area at 250 Cut Off for All Collagen phenotypes    -   skew for Filled Area at 250 Cut Off for All Collagen phenotypes    -   kurtosis for Filled Area at 250 Cut Off for All Collagen        phenotypes    -   Normalized count of Faint for Density at 0.18 Cut Off for All        Collagen phenotypes    -   Normalized count of Dense for Density at 0.18 Cut Off for All        Collagen phenotypes    -   mean for Density at 0.18 Cut Off for All Collagen phenotypes    -   median for Density at 0.18 Cut Off for All Collagen phenotypes    -   std for Density at 0.18 Cut Off for All Collagen phenotypes    -   skew for Density at 0.18 Cut Off for All Collagen phenotypes    -   kurtosis for Density at 0.18 Cut Off for All Collagen phenotypes    -   Normalized count of Low for Area to Perimeter Ratio at 2.8 Cut        Off for All Collagen phenotypes    -   Normalized count of High for Area to Perimeter Ratio at 2.8 Cut        Off for All Collagen phenotypes    -   mean for Area to Perimeter Ratio at 2.8 Cut Off for All Collagen        phenotypes    -   median for Area to Perimeter Ratio at 2.8 Cut Off for All        Collagen phenotypes    -   std for Area to Perimeter Ratio at 2.8 Cut Off for All Collagen        phenotypes    -   skew for Area to Perimeter Ratio at 2.8 Cut Off for All Collagen        phenotypes    -   kurtosis for Area to Perimeter Ratio at 2.8 Cut Off for All        Collagen phenotypes    -   Normalized count of Linear for Tortuosity at 1.1 Cut Off for All        Collagen phenotypes    -   Normalized count of Tortuous for Tortuosity at 1.1 Cut Off for        All Collagen phenotypes    -   mean for Tortuosity at 1.1 Cut Off for All Collagen phenotypes    -   median for Tortuosity at 1.1 Cut Off for All Collagen phenotypes    -   std for Tortuosity at 1.1 Cut Off for All Collagen phenotypes    -   skew for Tortuosity at 1.1 Cut Off for All Collagen phenotypes    -   kurtosis for Tortuosity at 1.1 Cut Off for All Collagen        phenotypes    -   Normalized count of Rounded for Eccentricity at 0.8 Cut Off for        All Collagen phenotypes    -   Normalized count of Elongated for Eccentricity at 0.8 Cut Off        for All Collagen phenotypes    -   mean for Eccentricity at 0.8 Cut Off for All Collagen phenotypes    -   median for Eccentricity at 0.8 Cut Off for All Collagen        phenotypes    -   std for Eccentricity at 0.8 Cut Off for All Collagen phenotypes    -   skew for Eccentricity at 0.8 Cut Off for All Collagen phenotypes    -   kurtosis for Eccentricity at 0.8 Cut Off for All Collagen        phenotypes    -   Normalized count of Porous for Solidity at 0.3 Cut Off for All        Collagen phenotypes    -   Normalized count of Compact for Solidity at 0.3 Cut Off for All        Collagen phenotypes    -   mean for Solidity at 0.3 Cut Off for All Collagen phenotypes    -   median for Solidity at 0.3 Cut Off for All Collagen phenotypes    -   std for Solidity at 0.3 Cut Off for All Collagen phenotypes    -   skew for Solidity at 0.3 Cut Off for All Collagen phenotypes    -   kurtosis for Solidity at 0.3 Cut Off for All Collagen phenotypes    -   Normalized count of Horizontal for Orientation at 45 Cut Off for        All Collagen phenotypes    -   Normalized count of Vertical for Orientation at 45 Cut Off for        All Collagen phenotypes    -   mean for Orientation at 45 Cut Off for All Collagen phenotypes    -   median for Orientation at 45 Cut Off for All Collagen phenotypes    -   std for Orientation at 45 Cut Off for All Collagen phenotypes    -   skew for Orientation at 45 Cut Off for All Collagen phenotypes    -   kurtosis for Orientation at 45 Cut Off for All Collagen        phenotypes    -   Normalized count of Dull for Skew at 3 Cut Off for All Fibrosis        Texture    -   Normalized count of Sharp for Skew at 3 Cut Off for All Fibrosis        Texture    -   mean for Skew at 3 Cut Off for All Fibrosis Texture    -   median for Skew at 3 Cut Off for All Fibrosis Texture    -   std for Skew at 3 Cut Off for All Fibrosis Texture    -   skew for Skew at 3 Cut Off for All Fibrosis Texture    -   kurtosis for Skew at 3 Cut Off for All Fibrosis Texture    -   Normalized count of Foggy for Kurtosis at 25 Cut Off for All        Fibrosis Texture    -   Normalized count of Crisp for Kurtosis at 25 Cut Off for All        Fibrosis Texture    -   mean for Kurtosis at 25 Cut Off for All Fibrosis Texture    -   median for Kurtosis at 25 Cut Off for All Fibrosis Texture    -   std for Kurtosis at 25 Cut Off for All Fibrosis Texture    -   skew for Kurtosis at 25 Cut Off for All Fibrosis Texture    -   kurtosis for Kurtosis at 25 Cut Off for All Fibrosis Texture    -   Normalized count of Blurry for Energy at 0.18 Cut Off for All        Fibrosis Texture    -   Normalized count of Resolved for Energy at 0.18 Cut Off for All        Fibrosis Texture    -   mean for Energy at 0.18 Cut Off for All Fibrosis Texture    -   median for Energy at 0.18 Cut Off for All Fibrosis Texture    -   std for Energy at 0.18 Cut Off for All Fibrosis Texture    -   skew for Energy at 0.18 Cut Off for All Fibrosis Texture    -   kurtosis for Energy at 0.18 Cut Off for All Fibrosis Texture    -   Normalized count of Homogeneous for Homogeneity at 0.35 Cut Off        for All Fibrosis Texture    -   Normalized count of Organized for Homogeneity at 0.35 Cut Off        for All Fibrosis Texture    -   mean for Homogeneity at 0.35 Cut Off for All Fibrosis Texture    -   median for Homogeneity at 0.35 Cut Off for All Fibrosis Texture    -   std for Homogeneity at 0.35 Cut Off for All Fibrosis Texture    -   skew for Homogeneity at 0.35 Cut Off for All Fibrosis Texture    -   kurtosis for Homogeneity at 0.35 Cut Off for All Fibrosis        Texture    -   Normalized count of Irregular for Correlation at 0.35 Cut Off        for All Fibrosis Texture    -   Normalized count of Patterned for Correlation at 0.35 Cut Off        for All Fibrosis Texture    -   mean for Correlation at 0.35 Cut Off for All Fibrosis Texture    -   median for Correlation at 0.35 Cut Off for All Fibrosis Texture    -   std for Correlation at 0.35 Cut Off for All Fibrosis Texture    -   skew for Correlation at 0.35 Cut Off for All Fibrosis Texture    -   kurtosis for Correlation at 0.35 Cut Off for All Fibrosis        Texture    -   Normalized count of Uniform for Inertia at 30 Cut Off for All        Fibrosis Texture    -   Normalized count of Contrasted for Inertia at 30 Cut Off for All        Fibrosis Texture    -   mean for Inertia at 30 Cut Off for All Fibrosis Texture    -   median for Inertia at 30 Cut Off for All Fibrosis Texture    -   std for Inertia at 30 Cut Off for All Fibrosis Texture    -   skew for Inertia at 30 Cut Off for All Fibrosis Texture    -   kurtosis for Inertia at 30 Cut Off for All Fibrosis Texture    -   Normalized count of Smooth for Entropy at 3.5 Cut Off for All        Fibrosis Texture    -   Normalized count of Rough for Entropy at 3.5 Cut Off for All        Fibrosis Texture    -   mean for Entropy at 3.5 Cut Off for All Fibrosis Texture    -   median for Entropy at 3.5 Cut Off for All Fibrosis Texture    -   std for Entropy at 3.5 Cut Off for All Fibrosis Texture    -   skew for Entropy at 3.5 Cut Off for All Fibrosis Texture    -   kurtosis for Entropy at 3.5 Cut Off for All Fibrosis Texture    -   Normalized count of Short for Length at 30 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Long for Length at 30 Cut Off for Fine        Collagen phenotypes    -   mean for Length at 30 Cut Off for Fine Collagen phenotypes    -   median for Length at 30 Cut Off for Fine Collagen phenotypes    -   std for Length at 30 Cut Off for Fine Collagen phenotypes    -   skew for Length at 30 Cut Off for Fine Collagen phenotypes    -   kurtosis for Length at 30 Cut Off for Fine Collagen phenotypes    -   Normalized count of Short for Total Skeleton Length at 27 Cut        Off for Fine Collagen phenotypes    -   Normalized count of Long for Total Skeleton Length at 27 Cut Off        for Fine Collagen phenotypes    -   mean for Total Skeleton Length at 27 Cut Off for Fine Collagen        phenotypes    -   median for Total Skeleton Length at 27 Cut Off for Fine Collagen        phenotypes    -   std for Total Skeleton Length at 27 Cut Off for Fine Collagen        phenotypes    -   skew for Total Skeleton Length at 27 Cut Off for Fine Collagen        phenotypes    -   kurtosis for Total Skeleton Length at 27 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Thin for Width at 5 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Thick for Width at 5 Cut Off for Fine        Collagen phenotypes    -   mean for Width at 5 Cut Off for Fine Collagen phenotypes    -   median for Width at 5 Cut Off for Fine Collagen phenotypes    -   std for Width at 5 Cut Off for Fine Collagen phenotypes    -   skew for Width at 5 Cut Off for Fine Collagen phenotypes    -   kurtosis for Width at 5 Cut Off for Fine Collagen phenotypes    -   Normalized count of Short for Perimeter at 100 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Long for Perimeter at 100 Cut Off for Fine        Collagen phenotypes    -   mean for Perimeter at 100 Cut Off for Fine Collagen phenotypes    -   median for Perimeter at 100 Cut Off for Fine Collagen phenotypes    -   std for Perimeter at 100 Cut Off for Fine Collagen phenotypes    -   skew for Perimeter at 100 Cut Off for Fine Collagen phenotypes    -   kurtosis for Perimeter at 100 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Small for Area at 240 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Large for Area at 240 Cut Off for Fine        Collagen phenotypes    -   mean for Area at 240 Cut Off for Fine Collagen phenotypes    -   median for Area at 240 Cut Off for Fine Collagen phenotypes    -   std for Area at 240 Cut Off for Fine Collagen phenotypes    -   skew for Area at 240 Cut Off for Fine Collagen phenotypes    -   kurtosis for Area at 240 Cut Off for Fine Collagen phenotypes    -   Normalized count of Small for Filled Area at 250 Cut Off for        Fine Collagen phenotypes    -   Normalized count of Large for Filled Area at 250 Cut Off for        Fine Collagen phenotypes    -   mean for Filled Area at 250 Cut Off for Fine Collagen phenotypes    -   median for Filled Area at 250 Cut Off for Fine Collagen        phenotypes    -   std for Filled Area at 250 Cut Off for Fine Collagen phenotypes    -   skew for Filled Area at 250 Cut Off for Fine Collagen phenotypes    -   kurtosis for Filled Area at 250 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Faint for Density at 0.18 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Dense for Density at 0.18 Cut Off for Fine        Collagen phenotypes    -   mean for Density at 0.18 Cut Off for Fine Collagen phenotypes    -   median for Density at 0.18 Cut Off for Fine Collagen phenotypes    -   std for Density at 0.18 Cut Off for Fine Collagen phenotypes    -   skew for Density at 0.18 Cut Off for Fine Collagen phenotypes    -   kurtosis for Density at 0.18 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Low for Area to Perimeter Ratio at 2.8 Cut        Off for Fine Collagen phenotypes    -   Normalized count of High for Area to Perimeter Ratio at 2.8 Cut        Off for Fine Collagen phenotypes    -   mean for Area to Perimeter Ratio at 2.8 Cut Off for Fine        Collagen phenotypes    -   median for Area to Perimeter Ratio at 2.8 Cut Off for Fine        Collagen phenotypes    -   std for Area to Perimeter Ratio at 2.8 Cut Off for Fine Collagen        phenotypes    -   skew for Area to Perimeter Ratio at 2.8 Cut Off for Fine        Collagen phenotypes    -   kurtosis for Area to Perimeter Ratio at 2.8 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Linear for Tortuosity at 1.1 Cut Off for        Fine Collagen phenotypes    -   Normalized count of Tortuous for Tortuosity at 1.1 Cut Off for        Fine Collagen phenotypes    -   mean for Tortuosity at 1.1 Cut Off for Fine Collagen phenotypes    -   median for Tortuosity at 1.1 Cut Off for Fine Collagen        phenotypes    -   std for Tortuosity at 1.1 Cut Off for Fine Collagen phenotypes    -   skew for Tortuosity at 1.1 Cut Off for Fine Collagen phenotypes    -   kurtosis for Tortuosity at 1.1 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Rounded for Eccentricity at 0.8 Cut Off for        Fine Collagen phenotypes    -   Normalized count of Elongated for Eccentricity at 0.8 Cut Off        for Fine Collagen phenotypes    -   mean for Eccentricity at 0.8 Cut Off for Fine Collagen        phenotypes    -   median for Eccentricity at 0.8 Cut Off for Fine Collagen        phenotypes    -   std for Eccentricity at 0.8 Cut Off for Fine Collagen phenotypes    -   skew for Eccentricity at 0.8 Cut Off for Fine Collagen        phenotypes    -   kurtosis for Eccentricity at 0.8 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Porous for Solidity at 0.3 Cut Off for Fine        Collagen phenotypes    -   Normalized count of Compact for Solidity at 0.3 Cut Off for Fine        Collagen phenotypes    -   mean for Solidity at 0.3 Cut Off for Fine Collagen phenotypes    -   median for Solidity at 0.3 Cut Off for Fine Collagen phenotypes    -   std for Solidity at 0.3 Cut Off for Fine Collagen phenotypes    -   skew for Solidity at 0.3 Cut Off for Fine Collagen phenotypes    -   kurtosis for Solidity at 0.3 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Horizontal for Orientation at 45 Cut Off for        Fine Collagen phenotypes    -   Normalized count of Vertical for Orientation at 45 Cut Off for        Fine Collagen phenotypes    -   mean for Orientation at 45 Cut Off for Fine Collagen phenotypes    -   median for Orientation at 45 Cut Off for Fine Collagen        phenotypes    -   std for Orientation at 45 Cut Off for Fine Collagen phenotypes    -   skew for Orientation at 45 Cut Off for Fine Collagen phenotypes    -   kurtosis for Orientation at 45 Cut Off for Fine Collagen        phenotypes    -   Normalized count of Short for Length at 30 Cut Off For Assembled        Collagen phenotypes    -   Normalized count of Long for Length at 30 Cut Off For Assembled        Collagen phenotypes    -   mean for Length at 30 Cut Off For Assembled Collagen phenotypes    -   median for Length at 30 Cut Off For Assembled Collagen        phenotypes    -   std for Length at 30 Cut Off For Assembled Collagen phenotypes    -   skew for Length at 30 Cut Off For Assembled Collagen phenotypes    -   kurtosis for Length at 30 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Short for Total Skeleton Length at 27 Cut        Off For Assembled Collagen phenotypes    -   Normalized count of Long for Total Skeleton Length at 27 Cut Off        For Assembled Collagen phenotypes    -   mean for Total Skeleton Length at 27 Cut Off For Assembled        Collagen phenotypes    -   median for Total Skeleton Length at 27 Cut Off For Assembled        Collagen phenotypes    -   std for Total Skeleton Length at 27 Cut Off For Assembled        Collagen phenotypes    -   skew for Total Skeleton Length at 27 Cut Off For Assembled        Collagen phenotypes    -   kurtosis for Total Skeleton Length at 27 Cut Off For Assembled        Collagen phenotypes    -   Normalized count of Thin for Width at 5 Cut Off For Assembled        Collagen phenotypes    -   Normalized count of Thick for Width at 5 Cut Off For Assembled        Collagen phenotypes    -   mean for Width at 5 Cut Off For Assembled Collagen phenotypes    -   median for Width at 5 Cut Off For Assembled Collagen phenotypes    -   std for Width at 5 Cut Off For Assembled Collagen phenotypes    -   skew for Width at 5 Cut Off For Assembled Collagen phenotypes    -   kurtosis for Width at 5 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Short for Perimeter at 100 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Long for Perimeter at 100 Cut Off For        Assembled Collagen phenotypes    -   mean for Perimeter at 100 Cut Off For Assembled Collagen        phenotypes    -   median for Perimeter at 100 Cut Off For Assembled Collagen        phenotypes    -   std for Perimeter at 100 Cut Off For Assembled Collagen        phenotypes    -   skew for Perimeter at 100 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Perimeter at 100 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Small for Area at 240 Cut Off For Assembled        Collagen phenotypes    -   Normalized count of Large for Area at 240 Cut Off For Assembled        Collagen phenotypes    -   mean for Area at 240 Cut Off For Assembled Collagen phenotypes    -   median for Area at 240 Cut Off For Assembled Collagen phenotypes    -   std for Area at 240 Cut Off For Assembled Collagen phenotypes    -   skew for Area at 240 Cut Off For Assembled Collagen phenotypes    -   kurtosis for Area at 240 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Small for Filled Area at 250 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Large for Filled Area at 250 Cut Off For        Assembled Collagen phenotypes    -   mean for Filled Area at 250 Cut Off For Assembled Collagen        phenotypes    -   median for Filled Area at 250 Cut Off For Assembled Collagen        phenotypes    -   std for Filled Area at 250 Cut Off For Assembled Collagen        phenotypes    -   skew for Filled Area at 250 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Filled Area at 250 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Faint for Density at 0.18 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Dense for Density at 0.18 Cut Off For        Assembled Collagen phenotypes    -   mean for Density at 0.18 Cut Off For Assembled Collagen        phenotypes    -   median for Density at 0.18 Cut Off For Assembled Collagen        phenotypes    -   std for Density at 0.18 Cut Off For Assembled Collagen        phenotypes    -   skew for Density at 0.18 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Density at 0.18 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Low for Area to Perimeter Ratio at 2.8 Cut        Off For Assembled Collagen phenotypes    -   Normalized count of High for Area to Perimeter Ratio at 2.8 Cut        Off For Assembled Collagen phenotypes    -   mean for Area to Perimeter Ratio at 2.8 Cut Off For Assembled        Collagen phenotypes    -   median for Area to Perimeter Ratio at 2.8 Cut Off For Assembled        Collagen phenotypes    -   std for Area to Perimeter Ratio at 2.8 Cut Off For Assembled        Collagen phenotypes    -   skew for Area to Perimeter Ratio at 2.8 Cut Off For Assembled        Collagen phenotypes    -   kurtosis for Area to Perimeter Ratio at 2.8 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Linear for Tortuosity at 1.1 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Tortuous for Tortuosity at 1.1 Cut Off For        Assembled Collagen phenotypes    -   mean for Tortuosity at 1.1 Cut Off For Assembled Collagen        phenotypes    -   median for Tortuosity at 1.1 Cut Off For Assembled Collagen        phenotypes    -   std for Tortuosity at 1.1 Cut Off For Assembled Collagen        phenotypes    -   skew for Tortuosity at 1.1 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Tortuosity at 1.1 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Rounded for Eccentricity at 0.8 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Elongated for Eccentricity at 0.8 Cut Off        For Assembled Collagen phenotypes    -   mean for Eccentricity at 0.8 Cut Off For Assembled Collagen        phenotypes    -   median for Eccentricity at 0.8 Cut Off For Assembled Collagen        phenotypes    -   std for Eccentricity at 0.8 Cut Off For Assembled Collagen        phenotypes    -   skew for Eccentricity at 0.8 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Eccentricity at 0.8 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Porous for Solidity at 0.3 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Compact for Solidity at 0.3 Cut Off For        Assembled Collagen phenotypes    -   mean for Solidity at 0.3 Cut Off For Assembled Collagen        phenotypes    -   median for Solidity at 0.3 Cut Off For Assembled Collagen        phenotypes    -   std for Solidity at 0.3 Cut Off For Assembled Collagen        phenotypes    -   skew for Solidity at 0.3 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Solidity at 0.3 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Horizontal for Orientation at 45 Cut Off For        Assembled Collagen phenotypes    -   Normalized count of Vertical for Orientation at 45 Cut Off For        Assembled Collagen phenotypes    -   mean for Orientation at 45 Cut Off For Assembled Collagen        phenotypes    -   median for Orientation at 45 Cut Off For Assembled Collagen        phenotypes    -   std for Orientation at 45 Cut Off For Assembled Collagen        phenotypes    -   skew for Orientation at 45 Cut Off For Assembled Collagen        phenotypes    -   kurtosis for Orientation at 45 Cut Off For Assembled Collagen        phenotypes    -   Normalized count of Dull for Skew at 1.25 Cut Off for All Tissue        Texture Normalized count of Sharp for Skew at 1.25 Cut Off for        All Tissue Texture    -   mean for Skew at 1.25 Cut Off for All Tissue Texture    -   median for Skew at 1.25 Cut Off for All Tissue Texture    -   std for Skew at 1.25 Cut Off for All Tissue Texture    -   skew for Skew at 1.25 Cut Off for All Tissue Texture    -   kurtosis for Skew at 1.25 Cut Off for All Tissue Texture    -   Normalized count of Foggy for Kurtosis at 2.5 Cut Off for All        Tissue Texture    -   Normalized count of Crisp for Kurtosis at 2.5 Cut Off for All        Tissue Texture    -   mean for Kurtosis at 2.5 Cut Off for All Tissue Texture    -   median for Kurtosis at 2.5 Cut Off for All Tissue Texture    -   std for Kurtosis at 2.5 Cut Off for All Tissue Texture    -   skew for Kurtosis at 2.5 Cut Off for All Tissue Texture    -   kurtosis for Kurtosis at 2.5 Cut Off for All Tissue Texture    -   Normalized count of Blurry for Energy at 0.15 Cut Off for All        Tissue Texture    -   Normalized count of Resolved for Energy at 0.15 Cut Off for All        Tissue Texture    -   mean for Energy at 0.15 Cut Off for All Tissue Texture    -   median for Energy at 0.15 Cut Off for All Tissue Texture    -   std for Energy at 0.15 Cut Off for All Tissue Texture    -   skew for Energy at 0.15 Cut Off for All Tissue Texture    -   kurtosis for Energy at 0.15 Cut Off for All Tissue Texture    -   Normalized count of Homogeneous for Homogeneity at 0.05 Cut Off        for All Tissue Texture    -   Normalized count of Organized for Homogeneity at 0.05 Cut Off        for All Tissue Texture    -   mean for Homogeneity at 0.05 Cut Off for All Tissue Texture    -   median for Homogeneity at 0.05 Cut Off for All Tissue Texture    -   std for Homogeneity at 0.05 Cut Off for All Tissue Texture    -   skew for Homogeneity at 0.05 Cut Off for All Tissue Texture    -   kurtosis for Homogeneity at 0.05 Cut Off for All Tissue Texture    -   Normalized count of Irregular for Correlation at 0.1 Cut Off for        All Tissue Texture    -   Normalized count of Patterned for Correlation at 0.1 Cut Off for        All Tissue Texture    -   mean for Correlation at 0.1 Cut Off for All Tissue Texture    -   median for Correlation at 0.1 Cut Off for All Tissue Texture    -   std for Correlation at 0.1 Cut Off for All Tissue Texture    -   skew for Correlation at 0.1 Cut Off for All Tissue Texture    -   kurtosis for Correlation at 0.1 Cut Off for All Tissue Texture    -   Normalized count of Uniform for Inertia at 300 Cut Off for All        Tissue Texture    -   Normalized count of Contrasted for Inertia at 300 Cut Off for        All Tissue Texture    -   mean for Inertia at 300 Cut Off for All Tissue Texture    -   median for Inertia at 300 Cut Off for All Tissue Texture    -   std for Inertia at 300 Cut Off for All Tissue Texture    -   skew for Inertia at 300 Cut Off for All Tissue Texture    -   kurtosis for Inertia at 300 Cut Off for All Tissue Texture    -   Normalized count of Smooth for Entropy at 3.5 Cut Off for All        Tissue Texture    -   Normalized count of Rough for Entropy at 3.5 Cut Off for All        Tissue Texture    -   mean for Entropy at 3.5 Cut Off for All Tissue Texture    -   median for Entropy at 3.5 Cut Off for All Tissue Texture    -   std for Entropy at 3.5 Cut Off for All Tissue Texture    -   skew for Entropy at 3.5 Cut Off for All Tissue Texture    -   kurtosis for Entropy at 3.5 Cut Off for All Tissue Texture

1. A method of computer aided phenotyping of fibrosis-relatedconditions, the method comprising: (a) receiving a digital image of abiological tissue sample, wherein the digital image indicates presenceof collagens in the biological tissue sample; (b) processing the imageto quantify a plurality of parameters, each parameter describing afeature of the collagens in the biological tissue sample that isexpected to be different for different phenotypes of fibrosis, whereinat least some of the features are selected from at least two of thegroup consisting of: (1) tissue level features that describe macroscopiccharacteristics of the collagens depicted in the digital image of thebiological tissue sample; (2) morphometric level features that describemorphometric characteristics of the collagens depicted in the digitalimage of the biological tissue sample; and (3) texture level featuresthat describe an organization of the collagens depicted in the digitalimage of the biological tissue sample, and wherein at least some of theplurality of parameters are statistics associated with histogramscorresponding to distributions of the associated parameters across atleast some of the digital image; and (c) combining at least some of theplurality of parameters in (b) to obtain one or more composite scoresthat quantify a phenotype of fibrosis for the biological tissue sample.2. The method of claim 1, wherein at least one of the features in (b) isa tissue level feature, at least another feature in the features of (b)is a morphometric level feature, and at least another feature in thefeatures in (b) is a texture level feature. 3-7. (canceled)
 8. Themethod of claim 1, wherein the method is compatible with any modality ofimaging that distinguishes between a presence and absence of collagensin the biological tissue sample.
 9. The method of claim 8, wherein themethod is compatible with at least stained histopathology slides, twophoton microscopy, fluorescence imaging, structured imaging, polarizedimaging, CARS, OCT images, fresh tissue imaging, and endoscopy.
 10. Themethod of claim 1, wherein the depiction of collagens in the biologicaltissue sample results from an optical marker that is specific to anyform of collagen.
 11. The method of claim 10, wherein the optical markeris a collagen-specific stain used in a histopathology method.
 12. Themethod of claim 1, wherein pixels of the digital image indicate presenceand quantity of collagens in corresponding volumes of the biologicaltissue sample. 13-16. (canceled)
 17. The method of claim 1, wherein atleast some of the parameters describing a morphometric level feature ora texture level feature is one of the statistics associated withhistograms that result from processing the histograms. 18-22. (canceled)23. The method of claim 17, wherein cut-off values split the histogramsinto subsets of sample values, and at least some of the statistics arefor a single subset of sample values. 24-28. (canceled)
 29. The methodof claim 1, wherein quantifying at least some of the plurality ofparameters associated with histograms comprises processing thehistograms to identify multiple modes of the histograms that areidentified by deconvoluting the histogram.
 30. (canceled)
 31. The methodof claim 29, wherein some modes of the histograms correspond tophenotypic signatures of the fibrosis-related conditions, whereindeconvoluting the histogram comprises filtering the histogram todetermine whether the histogram exhibits a phenotypic signature and toquantify the exhibited phenotypic signature. 32-34. (canceled)
 35. Themethod of claim 1, further comprising processing the digital image todistinguish between collagens in the biological tissue samplerepresented in the digital image.
 36. The method of claim 35, whereineach of the plurality of parameters used to obtain at least one of theone or more composite scores in (c) corresponds to one class ofcollagen.
 37. The method of claim 35, wherein collagen classes includeone or more of fine collagen, assembled collagen, and tissue regions.38. The method of claim 37, wherein each of the plurality of parametersused to obtain the at least one of the one or more composite scores in(c) corresponds to one collagen class. 39-45. (canceled)
 46. The methodof claim 36, wherein the collagen classes distinguish different stagesof fibrosis.
 47. The method of claim 1, wherein different features areselected to quantify different phenotypes of fibrosis. 48-52. (canceled)53. The method of claim 1, wherein the plurality of parameters that arecombined in (d) are selected from a list of candidate parameters using acalibration technique involving a calibration data set of calibrationdigital images taken from biological samples having known phenotypes offibrosis. 54-87. (canceled)
 88. The method of claim 1, wherein themethod quantifies the severity of fibrosis of the biological tissuesample, progression of fibrosis for the subject, regression of fibrosisfor the subject in response to a therapy, or type of fibrosis, on acontinuous scale. 89-111. (canceled)
 112. The method of claim 1, whereinthe parameters that describe texture level features include at least onestatistical parameter describing the distribution of one or moreproperties of the collagen image pixel intensity grey levelco-occurrence matrix (GLCM) defined on a spatial dimension across thecollagen image, the GLCM properties including at least one of the groupconsisting of: energy, homogeneity, contrast, correlation, inertia,entropy, skewness, and kurtosis.
 113. The method of claim 112, whereinthe texture level parameters are obtained by an image processing methodconfigured to quantify perceived texture of a collagen image.
 114. Themethod of claim 1, wherein the plurality of parameters that are combinedin (d) are selected from a set of candidate parameters to reduce thedimension of the set of candidate parameters.
 115. The method of claim113, wherein the plurality of parameters that are combined in (d) areselected using artificial intelligence and machine learning.
 116. Themethod of claim 1, wherein the image is received from a combination oftissue imaging methods that enrich the detection signal of the fibroustissue.
 117. The claim 116 wherein the imaging methods comprise stainedhistopathology slides, two photon microscopy, fluorescence imaging,structured imaging, polarized imaging, CARS, OCT images, fresh tissueimaging, and endoscopy.
 118. The method of claim 10, wherein the opticalmarker is an intrinsic bio-optical marker specific to one or morecollagens that is intrinsic to an optical imaging method.
 119. A methodof computer aided phenotyping of fibrosis-related conditions, the methodcomprising: (a) receiving a digital image of a biological tissue sample,wherein the digital image indicates presence of collagens in thebiological tissue sample; (b) processing the image to quantify one ormore parameters, each parameter describing a feature of the collagens inthe biological tissue sample that is expected to be different fordifferent phenotypes of fibrosis, wherein at least some of the featuresare selected from at least two of the group consisting of: (1) tissuelevel features that describe macroscopic characteristics of thecollagens depicted in the digital image of the biological tissue sample;(2) morphometric level features that describe morphometriccharacteristics of the collagens depicted in the digital image of thebiological tissue sample; and (3) texture level features that describean organization of the collagens depicted in the digital image of thebiological tissue sample, and wherein one or more of the parameters arestatistics associated with histograms corresponding to distributions ofthe associated parameters across at least some of the digital image; and(c) selecting one or combining more than one of the parameters in (b) toobtain one or more composite scores that quantify a phenotype offibrosis for the biological tissue sample.